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Job Description: Serve as a partner to the business or clinical area supported (AI Evaluation & Monitoring) Use analytic expertise to make data-driven decisions Collect, clean, and interpret data sets to solve problems Identify and communicate improvement opportunities Support evaluation of existing AI/ML projects Mentor junior BI developers through feedback and training Independently consult with stakeholders on evaluation and data requirements Execute the evaluation work for Geisinger's AI portfolio Collaborate in the design and development of reporting solutions Recommend analysis approach based on business questions Develop, validate, and execute complex queries and reports Present analysis and reports to internal customers Troubleshoot data quality issues when profiling, testing, or validating Requirements: Minimum 8 years of relevant experience Healthcare, clinical, or regulated-industry data experience strongly preferred Applied statistics: hypothesis testing, confidence intervals, power and sample size, regression, and non-parametric methods SQL with large, messy clinical data warehouses, including window functions, CTEs, debugging query plans, and performance tuning Python for analysis: pandas, numpy, scikit-learn, statsmodels, common evaluation libraries Production monitoring design and execution: drift detection, performance decay tracking, adoption and outcome metrics LLM and generative AI evaluation execution: golden sets, judge-based scoring, hallucination and grounding checks High School Diploma or Equivalent (GED) - Required Benefits: Healthcare benefits from day one, including vision and dental Atmosphere of collaboration, cooperation, and collegiality

Job Description: Define and evolve CFC-wide data architecture across all major systems and platforms Establish clear data domains, system boundaries, and integration patterns Assess current and future organizational data needs and translate them into architectural direction Develop long-term data architecture patterns that balance scalability, maintainability, and delivery constraints Design and maintain conceptual, logical, and physical data models representing CFC’s core entities, relationships, and measures Define, document, and govern source system schemas including canonical fields, data types, naming standards, and versioning approaches Establish systems of record and systems of reference for key domains such as accounts, licenses, users, and programs Ensure consistent implementation of data models across operational systems Create clear documentation that enables shared understanding across technical and non-technical stakeholders Architect and guide data flows between CFC systems to support operations, analytics, research, and decision-making Design integration and transformation approaches that preserve semantic meaning Oversee large-scale data integration, transformation, and storage patterns for reliable analytics Monitor and evolve data flows to reduce duplication, fragility, and unnecessary complexity Embed privacy, security, and compliance requirements directly into data models and integrations Partner with IT, security, and legal teams to align architecture with regulatory and policy requirements Ensure privacy and security protocols, including field-level encryption, role-based access control, deletion, and auditing are architected into and consistently supported across CFC’s data architecture Define data quality standards and validation strategies, including management of schema drift over time Enablement & Collaboration Work closely with engineers, analysts, researchers, data stewards, and business partners Serve as a subject-matter expert on CFC’s data architecture Communicate complex data concepts clearly to diverse audiences Requirements: 10+ years of experience designing and delivering data solutions, with at least 5 years in a data architecture role. Bachelor’s degree in computer science, computer engineering, or related field, or equivalent experience Extensive experience with relational databases and SQL 2+ years' experience with Lakehouse architectures, including Delta Lake technologies and medallion layer data modeling Demonstrated experience designing and maintaining enterprise-scale data models and source schemas Experience with data integration, transformation, and cleansing technologies Strong understanding of data security, privacy, and compliance principles Experience with cloud-based platforms (Azure preferred) Experience with custom-built data systems, semi-structured data, data quality issues, and data optimization. Ability to analyze undocumented data structures, identify source-to-target mappings, and uncover hidden data dependencies. Benefits: We cover 100% of your premiums for medical, dental and vision coverage and 50% for your dependent’s medical and dental premiums Retirement plan + company match up to 3% A flexible work subsidy: $100 per month that you can use on things like phone and internet costs, office supplies, or even commuting costs 16 paid holidays (including winter break and 1 floating holiday), 3 weeks' vacation in your first year, and separate sick leave accrual Other great benefits include: monthly contribution to childcare and/or dependent expenses, FSA account, parental leave, sabbatical leave, employee assistance program, annual wellness reimbursement, growth and development opportunities, disability and life insurance.

Job Description: Your engineering work will directly enable next-generation capabilities, including: Threat Detection Engine: Build advanced detections to identify threats early, including identity compromise, privilege escalation, impossible travel, and data exfiltration across identity, network, device, and session telemetry. ML Anomaly Detection: Production models using Isolation Forest, One-Class SVM, and Autoencoder neural networks to surface behavioral outliers that rules miss. Risk Aggregation & Enforcement: Design/develop accurate and explainable risk scoring systems that continuously normalize and correlate detection signals into dynamic user, device, and session risk scores that directly drive adaptive access enforcement decisions. Real-Time Detection Pipeline: Build scalable, low-latency streaming pipelines that process ZTNA events in near real time, enabling resilient, high-throughput security analytics. AI Agent Security: Define and implement security controls for autonomous AI agents, including detection of agent drift, unauthorized resource access, prompt injection attacks, privilege escalation, data leakage, and other emerging threats in Agentic AI systems. Autonomous Remediation (Roadmap): Leverage agentic AI to automate threat investigation, contextual analysis, and remediation workflows, enabling intelligent containment and response for high-confidence security incidents. Design and implement detection algorithms spanning authentication, authorization, network/location, data access, session management, and temporal behavioral domains. Train, evaluate, and deploy ML models on real-world identity and network telemetry; tune for production precision and recall targets. Architect and operate the detection pipeline — from audit log ingestion through risk aggregation and Risk Sentinel integration. Define the detection taxonomy — categorizing, prioritizing, and lifecycle-managing the full detection library using a scalable detection family model. Instrument and improve signal quality — measuring MTTD, false positive rates, and MITRE ATT&CK coverage; partnering with red teams to validate detections against real attack scenarios. Collaborate cross-functionally with security, product, and platform engineering to align detection coverage with customer threat models and roadmap priorities. Requirements: 7+ years of production AI/ML engineering experience, with a strong preference for candidates who have built threat detection, UEBA, ITDR, or identity security platforms at leading security or cloud companies. Detection algorithm expertise: Hands-on experience designing detections for identity-based threats — credential compromise, privilege escalation, insider activity, behavioral anomalies, and data exfiltration. ML proficiency: Experience building AI-powered security systems using large language models, deep learning, and agentic AI techniques for threat detection, anomaly analysis, contextual investigation, and intelligent remediation. Data & streaming engineering: Real-time or near-real-time pipeline experience (Kafka, Flink, Spark Streaming, or equivalent); familiarity with lakehouse formats (Apache Iceberg, Parquet). Security domain knowledge: MITRE ATT&CK, identity threat kill chains, ZTNA or network access control systems, and audit log analysis. Bonus: Experience with detection-as-code frameworks (Sigma, YARA), ZTNA platforms, LLMs or GNNs applied to security, or publications at USENIX, CCS, NeurIPS, or ICML. Mindset: Mission-driven, production-focused, signal-obsessed. You measure precision and recall, you eliminate alert fatigue, and you care that your work protects real systems.

Job Description: Work with Associate Data Scientists and Data Scientists in researching data sources, data cleansing, feature engineering, and creating baseline models Independently manage multiple data science model development/projects Collaborate and engage with various cross-functional stakeholders to ensure close coordination between data science and business stakeholders including the product management team Provide full ownership of the model development from inception to implementation including the machine learning pipeline, documentation, model drift, and remodeling strategy Mentor the Associate Data Scientists and Data Scientists in data science knowledge and their interactions with other stakeholders Coordinate with the ML team, anticipating deployment issues upfront, setting the stage for resource requirements, and ensuring the success of model deployment and retraining Take and delegate ownership of critical tickets or issues faced by business stakeholders as assigned by the Lead Data Scientist or Director of Data Science Read and independently navigate through extensive codebase with a focus on troubleshooting, debugging, and/or improvement of code Contribute to coding standards and coding conventions and proactively participate in enforcing sound coding practices across the data science team Create and maintain documentation of the ML models, data sources, and methodology Assist in the creation of feasibility documents Proactively participate in the core team processes including R&D, recommending, and implementing process improvements or new methodologies Share knowledge with other various teams and members by presentation at periodic internal team sessions/workshops Guide usage of development methodologies (Scrum, Kanban, etc.) and assist in their implementation in the group projects by deciphering details including features, tasks, estimates of efforts and timing, etc. Requirements: Master’s degree in data science/mathematics/CS/statistics or similar field and 3+ years of experience OR Bachelor's degree and 6+ years of experience in data science Effective written, verbal, and interpersonal communication skills, with the ability to work and communicate effectively with team members and cross-functional teams An expert-level understanding of the following topics: Classification, Regression, Clustering, Dimensionality reduction, Association rule learning, Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, Gradient Boosting), Neural Networks and Deep Learning, Model Evaluation and Metrics, Cross-validation, Hyperparameter Tuning, Optimization Algorithms, NLP and more Working knowledge of Agile/SCRUM methodologies with 1-2 years of experience and a practical understanding of the epics, features, and tasks that may be most relevant and how much effort is required for them Sufficient intellectual curiosity to investigate the latest developments in the field (for example LLM/Generative AI models) Ability to see the big picture and anticipate issues in terms of deployment limitations, data latency, appropriateness of a particular implementation of algorithms, and mathematical details/assumptions At least 1+ years of working with a major cloud platform, creating ML models using frameworks from data exploration to deployment Working knowledge in handling large datasets in a cloud environment Working knowledge of how APIs work, CI/CD (Continuous Integration/Continuous Deployment) Understanding of DevOps and MLOps principles 1+ years of writing production-grade code in scripting languages including Bash, Python, and SQL with the ability to identify the areas of improvement, optimal choice of data structures, optimization of the code, and creating custom code to address unique situations Proven, independent ability to navigate a data lake environment while creating training datasets or feature stores Effective skills in creating, deploying, and troubleshooting containers in the context of ML modeling 1+ years of experience with version control systems/concepts, such as Git, for tracking changes in machine learning projects Ability to investigate/research ML frameworks/algorithms, datasets, feature stores, and contribute to solving data science problems Capability of providing macro-level thought leadership to the junior members including the Data Scientists regarding best practices, issues, causes, and respective resolutions of those issues. Benefits: health insurance pre-tax spending accounts retirement benefits paid time off short-term and long-term disability employee stock purchase plan life insurance

WHAT WE DO FileScience is the business continuity layer for businesses that run on cloud platforms. Organizations across legal, healthcare, accounting, and architecture rely on us to stay operational through platform outages, accidental deletions, ransomware, and migration windows. When ransomware encrypts an entire M365 tenant, when a departing employee's account is auto-deleted and takes six months of client work with it, when the incident hits at 4pm Friday: we're the reason work doesn't stop. Under the hood, this is a data infrastructure problem at scale. You'd help build the ingestion engine and the recovery path that mirrors it: moving large volumes of customer data back into live tenants without dropping bytes, corrupting relationships, or violating tenant invariants. Some workloads run for minutes. Some run for weeks. THE ROLE FileScience is past the point where the founding team can keep scaling the platform alone. The core systems work. They got us here. Now they need to scale into a more mature, higher-throughput, more observable, more recoverable version of themselves. We are hiring a senior backend engineer to partner directly with the founding engineers on the systems that matter most: ingestion, scheduling, recovery, connector architecture, observability, CI/CD, and the internal tooling that lets a small team move quickly without losing correctness. This is a senior IC role with real architectural and product ownership. You will be expected to understand ambiguous production problems, shape the architecture, make tradeoffs, ship the implementation, and live with the consequences. You would be joining early enough to shape how the engineering team works, not just what it builds. The scope of the role grows with the company. WHAT YOU'D WORK ON These are real problems, in the codebase or in active design. You would not own all of this at once. The initial focus will depend on your background, but these are the systems the role can grow into. • Connectors and resource modeling across cloud platforms. Our connectors sit on top of APIs we do not control. Pagination that silently truncates. Cursors that advance after partial failures. Provider schemas that change and invalidate yesterday's assumptions. The goal is not "call the API." The goal is to build a durable model of customer data across cloud systems that were not designed to be modeled cleanly, and keep that model correct over time., • Multi-tenant scheduling under per-provider throttle constraints. Six cloud sources, each with its own throttle model: per-minute caps, daily budgets, concurrency limits, tenant-level quotas, undocumented backoff behavior, and provider-specific failure modes. Maximize throughput, stay fair across tenants, never breach a cap. The shape of the problem keeps changing every time a cloud ships an API update., • Recovery into live tenants. Backup is half the system. Restoring data into a live tenant means dealing with active user changes, object collisions, missing parents, permission drift, renamed folders, deleted users, schema changes, and partial restores. The recovery path mirrors the ingestion problem, but with higher stakes: instead of observing state, you are changing it., • Observability for week-long workloads. Standard distributed tracing was built for request-scoped operations. Our workloads run for days or weeks across multiple providers and tenants. Tracing, orchestration state, structured logs, and domain-specific correlation all have to work together to distinguish provider throttling from queue starvation from a poisoned resource from normal slowness., • Verification and developer leverage. Six external dependencies ship breaking changes when they feel like it. We use agentic tools throughout the dev loop. Both raise the bar for verification. Provider simulators, contract tests at connector boundaries, replay suites from production edge cases, staged gates from five-second pre-commit hooks to nightly runs. The goal is not process. The goal is leverage: make the safe path the fast path., • Internal tooling for a small team that ships above its weight. Deployment, local dev, test data generation, connector validation, replay harnesses, cloud fixtures, observability workflows, operational runbooks. If something is painful twice, it probably deserves automation. If a class of bug shows up repeatedly, it probably deserves a gate. We want someone who enjoys building the product and the machine that builds the product. HOW WE WORK We move quickly, but not casually. We like engineers who can read deeply, argue clearly, build pragmatically, and own the result. Some days that means designing a scheduler. Some days it means writing a migration. Some days it means digging three layers into a provider SDK to understand why a cursor advanced when it should not have. Some days it means deleting an abstraction that sounded good two months ago. Lightweight planning, no process theater. No heavy PM layer converting vague reality into tidy tickets. You will work directly with the founding team, customers, production systems, and the code. The tradeoff is that you get real context and real ownership. WHO WE'RE LOOKING FOR Someone who learns by building. The kind of engineer who reads a paper or a book and then goes and implements the idea. Who gets excited about hard systems problems and wants a team where the work stays interesting. This role assumes a few years of professional backend experience. We do not have a hard cutoff, but the work requires production-level judgment that usually comes from building and operating real systems. You might be a fit if you have: • Built backend or distributed systems where correctness mattered: data pipelines, recovery systems, workflow engines, schedulers, sync engines, observability systems, or infrastructure that customers depended on., • Owned production code beyond the first release. You have seen your own design survive traffic, incidents, migrations, weird edge cases, and the second or third version of the product., • Shaped problems, not just implemented tickets. You can figure out what needs to be built, explain the tradeoffs, and bring other engineers with you., • Worked deeply in Python at production scale., • Built on AWS systems such as ECS, Lambda, DynamoDB, SQS, CloudWatch, S3, or similar infrastructure., • Designed around partial failure, retries, idempotency, backpressure, caching, concurrency limits, queueing, or distributed state., • Debugged issues where the first obvious explanation was wrong., • Read code below the abstraction layer when the abstraction starts leaking., • Communicated clearly with a small team without needing heavy PM structure around you., • Helped other engineers get better, whether through code review, architecture discussions, pairing, or just raising the bar on what "done" looks like., • Gone unusually deep on at least one technical area because you genuinely wanted to understand it. Especially welcome: non-traditional paths, unusually deep side projects, production scars, open-source work, infra/tooling obsession, or anything else that shows you can go from curiosity to working systems. We care more about depth, judgment, and ownership than pedigree. LOGISTICS This is an NYC-based role. We are hiring for in-person partnership with the founding team. That does not mean corporate facetime or sitting in an office because someone said so. It means working shoulder-to-shoulder often enough to build quickly, argue through systems design in real time, and keep the feedback loop tight. That might be a coworking space, a Brooklyn apartment, a coffee shop, or wherever the team is getting good work done that day. If you want a pure async remote role, this is not the right fit. Compensation: $100,000-$150,000 base + equity participation + benefits. We know this is not big-company cash comp. The tradeoff is early ownership, meaningful equity participation, and the chance to shape core systems while the company is still small. We will walk through the full comp picture in conversation. BENEFITS • 100% employer-paid medical, dental, and vision coverage with co-pay reimbursement., • 2% interest rate reduction on your home mortgage., • 10 days vacation, 5 personal days, 5 sick days. Additional time awarded with tenure. Full benefits package at filescience.io/careers HOW TO APPLY Apply here. Optional: if there’s a technical problem you’ve gone unusually deep on, we’d love to hear about it. Production bug, side project, paper you implemented, system you reverse-engineered, infrastructure problem you kept improving after everyone else moved on - anything that shows how you think when something catches your attention. Send it to .

POS-9034 About the Team Commerce Hub is building the backbone for businesses to sell, bill, get paid, and manage revenue inside HubSpot. Our systems handle billions of dollars in transactions for our customers, so correctness, clarity, and predictability matter. We’re creating an AI-first commerce platform that’s deeply integrated with HubSpot’s CRM - simple, consistent, and extensible across every part of the seller and buyer experience. About the Role We’re looking for a Principal Software Engineer to help shape the technical future of Commerce Hub. This is a very hands-on role. You’ll write code, dive into services, review tough designs, debug messy systems, and directly own the architecture behind some of the most critical surfaces in CPQ, Billing, Payments, and Revenue OS. What You’ll Do Build a simple, consistent, extensible platform • Shape the technical roadmap across CPQ, Billing, and Payments with strong, opinionated patterns., • Define and evolve core domain models so pricing, products, contracts, invoices, and payments behave predictably everywhere., • Create frameworks that make the platform easy to extend without adding complexity. Be a deeply hands-on, high-leverage builder • Write code regularly. Lead design reviews and build high-impact systems end-to-end., • Own large multi-team initiatives that span CPQ, Billing and Payments., • Take prototypes to production at scale. Lead architectural direction • Set patterns for extensibility, contract boundaries, financial correctness, event-driven consistency, and how AI/agents safely interact with commerce objects., • Push for simplicity where the domain wants to get complicated, and consistency where surfaces can drift. Partner deeply with product leadership • Influence product strategy across CPQ, Buyer Portal, and Revenue OS., • Help teams make clear tradeoffs between speed, correctness, compliance, and long-term platform health. Raise the bar across the org • Mentor senior engineers and tech leads., • Drive thoughtful design decisions and guide learnings from incidents and large migrations., • Help teams align on patterns that reduce complexity and increase reliability. What You Bring • Proven experience building and scaling distributed systems with strict correctness requirements., • Background working with complex state machines, financial flows, or high-integrity data models., • Strong architectural instincts with a bias toward simplicity, consistency, and well-defined boundaries., • The ability to work horizontally across many teams and get alignment on platform patterns., • A habit of turning ambiguity into clear plans and working software—and staying hands-on every step of the way. Pay & Benefits The cash compensation below includes base salary, on-target commission for employees in eligible roles, and annual bonus targets under HubSpot’s bonus plan for eligible roles. In addition to cash compensation, some roles are eligible to participate in HubSpot’s equity plan to receive restricted stock units (RSUs). Some roles may also be eligible for overtime pay. Individual compensation packages are tailored to your skills, experience, qualifications, and other job-related reasons. ___ will help guide how we recommend thinking about the range you see. Learn more about HubSpot’s ___. Benefits are also an important piece of your total compensation package. Explore the ___ HubSpot offers to help employees grow better. At HubSpot, fair compensation practices aren’t just about checking off the box for legal compliance. It’s about living out our value of transparency with our employees, candidates, and community. Annual Cash Compensation Range:$313,800—$502,080 USD We know the ___ and___ can get in the way of meeting spectacular candidates, so please don’t hesitate to apply — we’d love to hear from you. If you need accommodations or assistance due to a disability, please reach out to us ___. At HubSpot, we value both flexibility and connection. Whether you’re a Remote employee or work from the Office, we want you to start your journey here by building strong connections with your team and peers. If you are joining our Engineering team, you will be required to attend a regional HubSpot office for in-person onboarding. If you join our broader Product team, you’ll also attend other in-person events such as your Product Group Summit and other gatherings to continue building on those connections. If you require an accommodation due to travel limitations or other reasons, please inform your recruiter during the hiring process. We are committed to supporting candidates who may need alternative arrangements Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Germany Applicants: (m/f/d) - link to HubSpot's Career Diversity page ___. India Applicants: link to HubSpot India's equal opportunity policy ___. About HubSpot HubSpot (NYSE: HUBS) is an AI-powered customer platform with all the software, integrations, and resources customers need to connect marketing, sales, and service. HubSpot's connected platform enables businesses to grow faster by focusing on what matters most: customers. At HubSpot, bold is our baseline. Our employees around the globe move fast, stay customer-obsessed, and win together. Our culture is grounded in four commitments: Solve for the Customer, Be Bold, Learn Fast, Align, Adapt & Go!, and Deliver with HEART. These commitments shape how we work, lead, and grow. We’re building a company___. We focus on brilliant work, not badge swipes. By combining clarity, ownership, and trust, we create space for big thinking and meaningful progress. And we know that when our employees grow, our customers do too. Recognized globally for our award-winning culture by Comparably, Glassdoor, Fortune, and more, HubSpot is headquartered in Cambridge, MA, with employees and offices around the world. Explore more: By submitting your application, you agree that HubSpot may collect your personal data for recruiting, global organization planning, and related purposes. Refer to HubSpot's ___ for details on data processing and your rights.

The Enterprise AI Platform Engineer is responsible for build and delivery of Natera’s enterprise agentic AI platform. The enterprise AI platform will be used to prototype and build multiple agentic AI solutions using low code across Natera in a federated approach.This is a hands-on technical leadership role at the intersection of engineering excellence, low code platform design, and applied GenAI engineering. You will architect and build the core AI operating system that powers modular, low-code enterprise AI agentic automation that is complete with agent templates, agent orchestration engine, data and MCP connectors, prompt optimization capabilities, evaluation guardrails, abstracted AI services, intelligent data extraction and reasoning capabilities. The platform empowers citizen developers, business analysts, and engineers to prototype and test AI-powered workflows using a low-code interface, while enabling developers to extend functionality through a pro-code framework. * Design: Design and implement the core architecture of the Enterprise AI Platform — low code, modular, scalable, and secure. * Agentic Orchestration: Build the agent orchestration runtime, including task queues, state management, and inter-agent communication. * Complexity: Architect for long-running, resilient AI workflows, enabling agents to execute and monitor multi-step, asynchronous processes. * Low code abstraction: Develop APIs and services for automation, evaluation, and agent lifecycle management. * Deployment: Establish DevOps, CI/CD pipelines, and configuration management to ensure smooth deployment at scale. * Low-Code Interface: Build an intuitive visual builder that allows business users to compose agent workflows through drag-and-drop and configuration. * Pro-Code Mode: Provide a developer extension layer where engineers can author and deploy agents in code (Python, TypeScript) directly into the same framework. * Unified Runtime: Ensure both low-code and pro-code workflows share common infrastructure for orchestration, evaluation, and governance. * Transparency & Debugging: Surface workflow traces, model evaluations, and output explanations directly in the user interface. * Experimentation & Versioning: Support iterative experimentation, evaluation-based comparison, and rollback through integrated version control. * Orchestration Engine: Build a robust orchestration system supporting both short-lived agent calls and long-running AI agents that persist over time to automate complex processes. * Workflow Automation: Enable orchestration of multiple agents with shared state, scheduling, dependency resolution, and event-driven execution. * Enterprise Integration: Connect agents to core enterprise systems to perform real-world actions securely. * Autonomy & Resilience: Implement mechanisms for persistence, checkpointing, recovery, and human-in-the-loop interventions. * Human in the Loop Feedback: Design human-in-the-loop and self-assessment mechanisms for continuous prompt and workflow improvement. * Evaluation as a Core Layer: Architect an evaluation-first framework for monitoring and improving AI agent performance across all workflows. * MCP Integration: Integrate with Model Context Protocols (MCPs) to enable plug-and-play connectivity with external systems and actions. * Retrieval-Augmented Generation (RAG): Build services to retrieve information from unstructured data using vector databases and retrieval pipelines. * Prompt Optimization & Evaluation: Implement automated systems for prompt tuning, evaluation, and feedback loops to ensure reliable results. * Abstracted AI Services: Build modular APIs for AI services such as unstructured document processing, information retrieval, information summarization, data extraction, content generation, classification etc. * Evaluation as a Core Layer: Architect an evaluation-first framework for monitoring and improving AI agent performance across all workflows. * Reusable Components: Create shared, composable AI primitives (e.g., document loaders, semantic routers, extractors) to accelerate workflow design. Governance, Security & Observability * Enforce governance, security, and compliance principles (SOC2, HIPAA, GDPR) across all platform operations. * Implement RBAC, audit logging, and lineage tracking for all data and agent interactions. * Build observability tools for tracing, cost monitoring, and system performance metrics. * Integrate evaluation-based guardrails that detect hallucinations, bias, or policy violations in real time. * Metric Tracking: Create structured metrics dashboards (precision, recall, task success rate, cost efficiency) for every deployed agent. Technical Leadership & Collaboration * Establish technical standards, documentation, and engineering patterns for future platform development. * Collaborate with business stakeholders, data scientists, and product teams to identify automation use cases and measure ROI via evaluation metrics. * Mentor future engineers and contribute to an engineering culture centered on safety, transparency, and impact. * Continuously explore emerging agent frameworks, vector stores, and evaluation methodologies. * 12+ years of software engineering experience, with 8+ years in platform or distributed systems architecture. * Proven expertise in implementing workflow orchestration or automation systems * Proficiency in Python with deep experience in backend architecture and API design. * Experience with low-code/no-code automation platforms (Zapier, n8n etc.), internal developer platforms (IDPs), or workflow engines (Temporal, Airflow). * Experience in working with well known agentic cloud platforms (e.g. AWS Bedrock agents, AgentCore etc.). * Hands-on experience with LLMs, RAG, vector databases, and orchestration frameworks (LangChain, LlamaIndex, AutoGen, DSPy). * Fluency in cloud infrastructure, Kubernetes, Docker, and CI/CD automation. * Knowledge of observability and telemetry systems for event-driven environments. * Familiarity with AI governance and evaluation frameworks, LLM safety, drift detection, bias detection, hallucination, explainability, and compliance (e.g., HIPAA, CLIA, FDA). Preferred: * Advanced degree (MS/PhD) in Computer Science, AI/ML, engineering or related field. * Experience in healthcare, pharma, diagnostics, or other regulated industries. The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations. Remote USA $174,400 - $218,000 USD OUR OPPORTUNITY Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives. The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management. WHAT WE OFFER Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program! For more information, visit ___. Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide. All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability or any other legally protected status. We also consider qualified applicants regardless of criminal histories, consistent with applicable laws. If you are based in California, we encourage you to read this important information for California residents. Link: ___ Please be advised that Natera will reach out to candidates with a @___ email domain ONLY. Email communications from all other domain names are not from Natera or its employees and are fraudulent. Natera does not request interviews via text messages and does not ask for personal information until a candidate has engaged with the company and has spoken to a recruiter and the hiring team. Natera takes cyber crimes seriously, and will collaborate with law enforcement authorities to prosecute any related cyber crimes. For more information:

Knowledge of machine learning implementation strategies such as: proper and thorough evaluation of ML models in production, detecting data/covariate/concept drift, leveraging feature stores and model registries, deploying models as REST APIs, integrating models into products, etc. The Principal Data

Job Description StubHub is on a mission to redefine the live event experience on a global scale. Whether someone is looking to attend their first event or their hundredth, we're here to delight them all the way from the moment they start looking for a ticket until they step through the gate. The same goes for our sellers. From fans selling a single ticket to the promoters of a worldwide stadium tour, we want StubHub to be the safest, most convenient way to offer a ticket to the millions of fans who browse our platform around the world. The Role: We're seeking a Data Scientist 2 to lead the science behind a best-in-class ticketing experience for fans, with a focus on the operational journey from ticket purchase through event entry. In this role, you will own and develop predictive and decisioning models that proactively identify and mitigate fulfillment issues, strengthen inventory validation, and optimize customer service policies — all while balancing fan experience with operational constraints. You will work on problems where getting it right matters: predicting issues before they impact fans, identifying emerging pain points at scale, and turning moments of potential failure into opportunities to build trust and loyalty. This includes building and productionizing models on both structured and unstructured data, such as LLM-backed pipelines that extract actionable signals from customer communications and support interactions. You will partner closely with product managers, analytics engineers, operations leaders, and other data scientists to translate ambiguous business problems into durable, production-ready data products, and to influence decisions that measurably improve the fan experience. Location: Hybrid (3 days in office/2 days remote) – New York, NY or Century City, CA What You'll Do: • Own the science behind ticket fulfillment: Drive reliability, ease of use, and customer satisfaction by crafting and deploying scalable, production-grade machine learning models that directly inform operational systems and decisions., • Drive data-informed strategy: Work with operations partners to shape roadmaps, using analysis and modeling to identify, prioritize, and size high-impact opportunities that can be implemented and sustained at scale., • Design for validation: Apply principles of experimentation and causal inference to ensure work products can be rigorously evaluated, defining offline validation and online testing strategies early in the design process., • Build the data foundation: Partner with platform and analytics teams to ensure data availability and quality, including event instrumentation, batch and streaming ETL, feature stores, and monitoring for model performance, bias, and drift., • 2-4 years of industry experience in data science or machine learning, with an MS or PhD in mathematics, statistics, computer science, or a related quantitative field preferred., • Strong programming skills in Python, with experience using numerical and machine learning libraries such as Pandas, NumPy, SciPy, scikit-learn, and gradient-boosting frameworks (e.g., LightGBM, XGBoost)., • Demonstrated experience building and iterating on machine learning models, including rigorous model validation and evaluation, • Expertise working with large-scale data using modern analytics and compute platforms such as Snowflake, BigQuery, or Databricks, with strong proficiency in SQL., • Experience applying machine learning to operational problem spaces such as ticket fulfillment, fraud detection, customer service, or trust and safety., • Experience with production ML platforms and workflows, such as Metaflow or comparable systems (e.g., Airflow-based ML pipelines, Kubeflow, SageMaker Pipelines, Prefect, Flyte), including model versioning, scheduling, and monitoring., • Accelerated Growth Environment: An environment designed for swift skill and knowledge enhancement, where you have the autonomy to lead experiments and tests on a massive scale., • Top Tier Compensation Package: Competitive base, equity, and upside that tracks with your impact., • Flexible Time Off: Enjoy unlimited Flex Time Off, giving you the flexibility to manage your schedule and take time to recharge as needed.

Octave is a modern behavioral health practice creating a new standard for care delivery that’s both high-quality and accessible. With in-person and virtual clinics in multiple states, the company offers evidence-based individual, couples, and family therapy, while pioneering relationships with payers to make care more affordable through insurance. By raising the bar on how care is delivered and how providers are supported, we are building a sustainable system that values equity, affordability, and effectiveness. We’re looking for a Sr. Data Engineer with strong data platform experience to help evolve our modern data stack and contribute to the foundation of our emerging AI and ML platform. This role sits at the intersection of data engineering, platform architecture and machine learning enablement and will bring high-quality, scalable, and ethical AI into real-world use. You will partner closely with data scientists, analysts, and product managers to ensure our platform supports reliable data pipelines, scalable analytics, and production ready machine learning systems in addition to defining new architecture, best practices, and patterns for fellow engineers to inherit. The ideal candidate is both a systems thinker and a hands-on builder who thrives in evolving environments and is passionate about creating reliable data infrastructure that enables peers and partner teams to move faster with data. • Design, build, and maintain scalable systems for ingestion, transformation, and storage of data, with a focus on testing and observability., • Implement frameworks, tooling, and automation to safely increase development velocity., • Develop foundational end-to-end AI/ML workflows from (1) source ingestion and preparation, (2) training and tuning, (3) experimentation and productionization, and (4) downstream systems integration (EHR modules, micro-services, dashboards)., • Support iterative model development and production operations and observability (accuracy, drift, bias, fairness, reproducibility)., • Contribute to a culture of continuous improvement, knowledge-sharing and mentoring of peer engineers., • Bachelor’s degree (or equivalent) in Computer Science, Data Science, Statistics, Engineering or a related field., • 5+ years of experience in data engineering, platform engineering, or ML engineering., • Experience working with major cloud data platforms and tools:, • Preferred experience:, • Healthcare, behavioral health, EHR systems, and/or regulated industries., • Specific expertise with: AWS/GCP, dbt, AirflowAirbyte, Redshift/BigQuery., • Proficiency in SQL and Python with strong familiarity towards modern data engineering frameworks, infrastructure, and tooling., • Proficiency with data ops best practices, monitoring, pipeline automation, and CI/CD., • Knowledge of modern compute and ML frameworks/libraries (i.e., Spark, TensorFlow, PyTorch, scikit-learn)., • Ability to build production APIs and services, inclusive of MCP servers that expose internal data/services to LLMs., • A collaborative mindset, dependable execution, drive to reflect and improve, and humility to ask questions and learn. The below values drive our day-to-day operations. • We’re human beings first. We operate with empathy and kindness – with our clients, with our collaborators, and with ourselves., • People deserve better than status quo. We’re willing to tackle the intractable problems, no matter how big, because someone should. We ask big questions, we craft big solutions, and we challenge ourselves and others to make it happen., • No bystanders. No stars. No tourists. Each person has been selected to be here, and with that comes a responsibility to bring your expertise, share your ideas, and help make this company better., • Partnership paves the path ahead. We don’t operate in a silo, internally or externally. To transform the system, we believe in working with others to create something bigger, better, and stronger., • Quality is crucial at scale. Quality is core to our business, and we refuse to sacrifice it as we grow., • Progress is a process. In the pursuit of progress, we iterate, reflect, learn, adjust – and always leave things better than we found them., • There are people behind every data point. We recognize that numbers tell only one part of the story, and we also do the work to understand impacts at the individual level., • Prolonged periods sitting at a desk and working on a computer., • Must be able to frequently communicate with others through virtual meeting applications such as Zoom and Google Meet., • Must be able to observe and communicate information on company provided laptop., • Move up to 10 pounds on occasion., • Must be eligible to work in the United States without sponsorship now or in the future. Octave is committed to pay equity. To maintain our commitment to pay equity, Octave will follow Pay Transparency regulations on all open job postings. Current Pay Transparency laws require companies to include a position's salary or hourly wage range (not including bonuses or equity-based compensation) in any internal or external job posting. This requirement extends to job postings published by a third party at an employer's request. Octave will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with Octave’s legal duty to furnish information. Starting pay for qualified applicants will depend on a combination of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected salary range for this role is set forth below and this range may be modified in the future. The salary range for Geo 1 (all states, excluding those in Geo 2 or Geo 3, and D.C.) is $142,600 - $153,100. The salary range for Geo 2 (CO, HI, MD, RI) is $156,900 - $168,400. The salary range for Geo 3 (AK, CA, CT, MA, NJ, NY, WA) is $164,000 - $176,000. All Geos are eligible for equity in the form of stock options. Additionally, this position is eligible for the following benefits: company sponsored life insurance, disability and AD&D plans. Voluntary benefits such as 401k retirement, medical, dental, vision, FSA, HSA, dependent care and commuter/parking options are also available. Octave offers generous Paid Time Off as well as paid parental leave benefits. This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice. As part of our hiring process, we may use technology tools, including AI-supported systems, to assist with reviewing applications or documenting interviews. These tools are designed to support our team, not replace human judgment, and final hiring decisions are always made by our team. This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice. Application Instructions: Please complete the following application. Please note that the U.S. Equal Opportunity Employment Information questions below are used for the purposes of EEOC reporting and are optional to complete. Octave is unable to change these questions and we acknowledge that many of the U.S. Equal Opportunity Employment Information questions are not inclusive or affirming of all aspects of cultural identity. Octave is committed to an inclusive workplace environment, and this information will not inform how we approach hiring or employment.

Knowledge of machine learning implementation strategies such as: proper and thorough evaluation of ML models in production, detecting data/covariate/concept drift, leveraging feature stores and model registries, deploying models as REST APIs, integrating models into products, etc. The Senior Data Sc

Job Description About impact.com impact.com is the world's leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products - Performance (affiliate), Creator (influencer), and Advocate (customer referral) - unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands - including Walmart, Uber, Shopify, Lenovo, L'Oréal, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results. Your Role at impact.com: We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group. You'll own the design and deployment of machine learning models that optimize yield, pricing, and inventory allocation at scale — sitting at the intersection of data science, platform engineering, and marketplace economics. This is a high-craft, high-ownership individual contributor role. You'll work end-to-end: architecting the data pipelines that feed your models, engineering the features that drive performance, and deploying real-time inference systems that make decisions at speed. Your work directly determines how effectively Impact's programmatic marketplace balances advertiser performance with publisher monetization — making this one of the highest-leverage technical roles in the business. You'll collaborate closely with Product, Data Science, and Programmatic Delivery Engine Engineering, but you operate with significant autonomy. You're expected to bring both the modeling rigor of a data scientist and the production instincts of an ML engineer — and to be genuinely excited about both. What You'll Do: Yield Optimization & Pricing Models • Design and deploy ML models that optimize auction pricing, bid shading, floor price setting, and yield across Impact's programmatic inventory., • Build and iterate on real-time pricing algorithms that balance short-term revenue efficiency with long-term publisher and advertiser health., • Develop and maintain feedback loops that allow pricing models to adapt to shifting market conditions, inventory mix, and demand patterns., • Own ML-driven inventory allocation logic: routing, pacing, and matching supply to demand across partner segments, deal types, and campaign objectives., • Build models that forecast inventory availability, demand curves, and clearing prices to support proactive allocation decisions., • Design and own the data infrastructure that feeds programmatic models: event pipelines, feature stores, training datasets, and real-time feature serving., • Engineer high-signal features from auction logs, bid stream data, user signals, contextual attributes, and historical performance — at the scale of programmatic data volumes., • Deploy models to production real-time inference environments; own latency, reliability, and throughput requirements for auction-time decision-making., • Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers., • Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic., • Design and execute rigorous A/B and holdout experiments to measure the causal impact of model changes on yield, fill rate, advertiser performance, and publisher revenue., • Build evaluation frameworks that go beyond offline metrics — validating model behavior in live auction environments where feedback signals are delayed or noisy., • Research and implement adaptive, self-learning components within the programmatic stack — including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate., • Design feedback mechanisms that close the loop between auction outcomes, model updates, and system behavior; reduce reliance on manual tuning and rule-based overrides., • Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business requirements into modeling approaches and communicate technical tradeoffs clearly., • Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse. Required • Experience: 5+ years in data science, ML engineering, or quantitative research, with at least 2+ years building and deploying ML models in programmatic advertising, ad tech, marketplace optimization, or a closely related domain (e.g., real-time bidding, dynamic pricing, auction systems)., • Programmatic & marketplace depth: Demonstrated understanding of programmatic auction mechanics (RTB, header bidding, floor pricing, deal types, bid shading) and how ML can be applied to optimize outcomes across the supply-demand stack., • Production ML engineering: Proven ability to take models from prototype to production independently — including real-time inference, monitoring, retraining pipelines, and SLO ownership., • Data architecture: Experience designing and building data pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems., • Technical skills:, • Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and large-scale data tools (Spark, Kafka, or equivalent streaming/batch frameworks)., • Experience with real-time feature serving and low-latency model deployment (REST APIs, gRPC, or streaming inference)., • Familiarity with production ML workflows: model versioning, drift monitoring, A/B testing, evaluation, and retraining., • Experience processing and modeling at programmatic data scale: high-cardinality auction logs, bid stream data, impression and click events., • Experimentation rigor: Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques)., • Communication: Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums., • Direct experience with SSP, DSP, or exchange-side yield optimization — particularly floor price optimization, bid landscape modeling, or deal matching algorithms., • Familiarity with auction theory (first-price vs. second-price dynamics, optimal reserve pricing, revenue equivalence) and its practical implications for programmatic ML., • Experience with contextual bandits, multi-armed bandits, or reinforcement learning applied to real-time decisioning problems., • Knowledge of online learning and adaptive algorithms in production environments with non-stationary data distributions., • Familiarity with privacy-preserving ML techniques relevant to programmatic (differential privacy, federated learning, cookieless attribution modeling)., • Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training., • Exposure to supply forecasting, inventory management, or capacity planning in programmatic or marketplace contexts., • Marketplace intuition. You understand programmatic auctions not just as an engineer but as an economist — you think about incentive structures, equilibrium dynamics, and how model decisions ripple through the supply-demand stack., • Full-stack ML ownership. You're as comfortable designing a feature store schema as you are tuning a gradient boosting model or debugging a latency spike in production. You own the whole chain., • Feedback loop thinking. You don't just deploy models — you design the systems that make them smarter over time. You think about how today's decisions become tomorrow's training signal., • Rigor under real-world constraints. You know how to run clean experiments in environments where feedback is delayed, data is noisy, and business pressures create tradeoffs. You don't let imperfect conditions become an excuse for imprecise thinking., • Pragmatic delivery. You ship. You balance the perfect with the production-ready, iterate fast, and know when an MVP outperforms a six-month research project. *This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time. Benefits and Perks: At impact.com, we believe that when you're happy and fulfilled, you do your best work. That's why we've built a benefits package that supports your well-being, growth, and work-life balance. • Medical, Dental, and Vision insurance, • Office-only catered lunch every Thursday, a healthy snack bar, and great coffee to keep you fueled, • Flexible spending accounts and 401(k), • Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life., • Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health., • A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval., • Investing in Your Growth: We're committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses., • Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver. #LI_NewYork_NewYork

Job DescriptionAbout CookUnity: Food has lost its soul to modern convenience. And with it, it has lost the power to nourish, inspire, and connect us. So in 2018, CookUnity was founded as the first-of-its-kind platform that connects the world with the source of truly great food: chefs. Today, CookUnity delivers 50 million meals a year from the industry's best chefs to homes all over the country. Fresh. Ready-to-eat. And crafted with the passion that nourishes body and soul. Unwilling to stop there, CookUnity is expanding beyond delivery to become an ever-innovating marketplace focused on our singular mission: empower Chefs to nourish the world. If that mission has you hungry in more ways than one, you've found the right job posting. About the Role We're hiring for an experienced ML-focused Data Scientist to own growth-oriented modeling across the customer lifecycle - acquisition, activation, retention, resurrection and monetization. You'll work alongside other data scientist to design, validate, and productionize statistical and ML systems (pLTV, churn/survival, uplift/incrementality, lookalikes, clustering/embeddings, NBA/ranking) that directly drive growth. This is a hands-on role for someone with strong mathematical/statistical foundations, broad modeling experience, and pragmatic MLOps chops who can lead experiments and partner closely with Marketing, Engineering, CRM, and Product. What you'll do… • Lifecycle modeling: Build and maintain predictive LTV, churn (including survival/time-to-event), order-rate, and resurrection models that feed acquisition, CRM, and retention strategies., • Acquisition & lookalikes: Create lookalike / propensity models for paid channels and audience construction; optimize CAC vs LTV tradeoffs., • Next-Best-Action & personalization: Develop NBA/ranking models, small-scale recommenders and embedding-based similarity systems to increase activation and orders., • Unsupervised & representation learning: Lead segmentation, clustering, embeddings and representation work that create actionable cohorts and features., • Production & MLOps: Own the full model lifecycle - training pipelines, CI/CD, model registries, containerized deployment, monitoring, retraining and drift detection; partner with engineers to operationalize models into CRM, marketplace and paid channels., • Model governance & reproducibility: Ensure models are well-tested, explainable, calibrated, and auditable; document assumptions, limitations and business mappings., • Cross-functional influence: Translate technical work into product recommendations, dashboards and clear narratives for Growth, Marketing and Engineering. Mentor peers and raise modeling and MLOps standards.Required qualifications, • 5-8+ years in data science, applied ML or statistics, with a track record of shipping production models., • Strong math & statistics: probability, inference, regression, survival analysis/time-to-event, causal reasoning, and familiarity with statistical modeling tradeoffs., • End-to-end ML experience: experience building, validating and deploying classification/regression/ensemble/deep models; comfort with embeddings and representation learning., • MLOps & production skills: pragmatic experience with model CI/CD, model registries (MLFlow or similar), containerization (Docker), orchestration (Airflow), and runtime infra (K8s / ECS)., • Software engineering & tooling: Python (pandas, scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow optional), strong code hygiene, testing and reproducibility., • Product & stakeholder collaboration: excellent communication, ability to embed with Growth/CRM/Marketing and translate models into product decisions., • Education: BS in a quantitative field required; MS/PhD in statistics, math, CS, economics or similar preferred.Nice-to-haves, • Experience in subscription marketplaces, food-tech, or consumer marketplaces., • Familiarity with feature stores, Snowflake/BigQuery, and production monitoring tools., • Experience with causal libraries (EconML), uplift frameworks, or survival modeling packages., • Prior work on small-scale recommender systems, embeddings, or NLP personalization. Learn More About CookUnity We believe great leadership starts with alignment on vision, values, and ways of working. To give you deeper insight into who we are and what we're looking for, we invite you to explore: CookUnity's Leadership Principles – The values and behaviors that guide how we operate, collaborate, and scale. We hope this provides valuable insight into our culture and product vision. If this excites you, we'd love to connect! Benefits 🩺 Health Insurance coverage 🌅 401k Plan ⛱ Unlimited PTO 🗓️ 5- year Sabbatical: After 5 years with CookUnity, you get a 4-week paid sabbatical 🐣 Paid Family leave 🕯 Compassionate Leave: 3-5 days each time the need arises 🥘 A generous amount of CookUnity credits to enjoy our amazing meals, added to your account, monthly 🧘🏽️‍♀️ Wellness perks: access to a nutritional coach and fitness subsidies to build a healthy lifestyle 👩🏾️‍🏫 Personalized Spanish coach 🌟 Awesome opportunity to join a company that is looking to change how we eat and how chefs work! Compensation All final pay rates will be determined by candidates experience, knowledge, skills, and abilities of the applicant, internal equity, and alignment with market data.Pay Range for this position$160,000—$185,000 USD If you're interested in this role, please submit your application, and if we think you might be a fit, we'll get in touch with you. Thank you for your time! CookUnity is an Equal Opportunity Employer. We are dedicated to creating a community of inclusion and an environment free from discrimination or harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, sexual orientation, gender identity, national origin, citizenship status, protected veteran status, genetic information, or physical or mental disability. A quick note for all candidates We've recently seen an increase in recruitment scams across the industry, and we want to make sure you (and your data) stay safe while applying to CookUnity. We also want you to know that we take this seriously — sometimes, as part of our process, we may ask for a brief "proof of humanity" to confirm that we're connecting with a real person, not an impersonator. Here are a few tips to help you protect yourself and know what to expect from us: • Apply only through our official channels. All open roles are listed on our official careers page: careers.cookunity.com, • Our recruiters are real people — and easy to verify. You can always find them on LinkedIn with verified profiles. If you're unsure, feel free to reach out to us on our official LinkedIn Company Page., • We only communicate through official CookUnity channels. That means emails ending in @cookunity.com and interviews held through official company platforms (Google Meet or Zoom) — never WhatsApp, Telegram, or SMS.

Job DescriptionAbout Us We are building the next generation of tax-advantaged separately managed accounts (SMAs) — a platform that fuses institutional-grade quantitative research, scalable technology, and hands-on client expertise. Our mission is to deliver hyper-personalized, tax-aware portfolios at scale. We're not looking for a “relationship manager.” We’re looking for an investment athlete who can sit at the intersection of clients, quants, and code — people who are fluent in markets, comfortable in Python, and credible in front of sophisticated allocators. The Role As a Senior Client Portfolio Manager (CPM), you’ll translate quantitative insights into client outcomes and answers. You’ll partner with portfolio managers, engineers, and customer success representatives, to customize portfolios, explain performance and risk, and help scale a platform that is redefining wealth management. This is a high-visibility role where you’ll be trusted to: • Engage directly with clients/advisors as an ambassador of the SMA platform., • Interpret portfolio data — exposures, tax trades, drift, risk — and explain in a way clients can act on., • Run analyses in Python (pull time series, diagnose factor exposures, validate tax trades, build quick diagnostic dashboards)., • Collaborate with research and engineering teams to translate client needs into features and workflow improvements., • Proactively monitor accounts for opportunities: tax-loss harvesting, transitions, customization, compliance with restrictions. What We're Looking For • 3+ years experience in asset management, wealth management, or investment consulting with direct client responsibility., • Strong command of equity markets, factor risk, and tax-aware investing., • Hands-on Python skills — you don’t need to be an engineer, but you must be able to slice data, run analyses, and speak the same language as quants and developers., • Ability to context-switch between technical deep dives and client-ready narratives., • Proven record of earning trust with sophisticated clients/advisors., • CFA charterholder, or equivalent advanced credential preferred. Why Join • Impact: Help define the future of tax-aware SMAs., • Exposure: Work alongside leading investment, quant, and fintech professionals., • Growth: Rapid career trajectory in a high-visibility, high-growth business.

Job Description Datavant is the data collaboration platform trusted for healthcare. Guided by our mission to make the world's health data secure, accessible and actionable, we provide critical data solutions for organizations across the healthcare ecosystem - including providers, health plans, researchers, and life sciences companies. From fulfilling a single patient's request for their medical records to powering the AI revolution in healthcare, Datavanters are building the future of how data is connected and used to improve health. By joining Datavant today, you're stepping onto a driven and highly collaborative team that is passionate about creating transformative change in healthcare. What We're Looking For We are seeking a Senior Cloud Security Engineer to architect and operationalize Datavant's enterprise Edge Defense strategy within the Secure Product & Infrastructure program. This role is accountable for defining long-term WAAP architecture, standardizing protection patterns across verticals, and ensuring scalable, automated coverage for all internet-facing workloads. You will operate at a systems level and serve as a senior security partner to Engineering teams—shaping architectural direction, influencing roadmap decisions, and ensuring edge protection scales with the business. We are looking for a true senior-level engineer who can own program-level outcomes, not just implement controls. What You Will Do • Define and evolve the enterprise Edge Defense architecture across cloud-native, CDN-layer, and hybrid environments., • Lead cross-vertical rollout of standardized WAAP patterns, replacing fragmented or manually managed implementations with automated, enforceable models., • Drive vendor evaluation, integration, and lifecycle governance for WAAP technologies., • Establish infrastructure-as-code and policy-as-code frameworks that make secure edge protection the default path for Engineering teams., • Partner deeply with Platform and Product Engineering during system design to prevent downstream security debt., • Define and track program-level KPIs for edge protection, including coverage, enforcement maturity, drift reduction, onboarding velocity, and detection quality., • Deep experience designing and operating WAAP or WAF controls at scale across multi-account and multi-environment deployments., • Proven ability to architect security programs, including governance models, automation strategy, lifecycle management, and cross-team alignment., • Strong infrastructure-as-code expertise and experience building reusable modules and guardrails adopted across teams., • Demonstrated success serving as a trusted, customer-oriented security partner to Engineering leadership., • Ability to influence without authority and navigate competing priorities across verticals., • Strong operational instincts and experience designing for long-term maintainability and response integration., • Comfort operating in regulated environments and designing controls that scale across commercial and compliance-constrained systems., • You have architected or materially transformed a WAAP or edge protection program across multiple engineering verticals., • You have led vendor migrations or platform consolidations in the edge protection space., • You have authored Architecture Decision Records (ADRs) that shaped long-term security architecture and influenced senior engineering leadership., • You have defined measurable security KPIs that guided roadmap investment and engineering alignment. At Datavant our total rewards strategy powers a high-growth, high-performance, health technology company that rewards our employees for transforming health care through creating industry-defining data logistics products and services. The range posted is for a given job title, which can include multiple levels. Individual rates for the same job title may differ based on their level, responsibilities, skills, and experience for a specific job. The estimated total cash compensation range for this role is:$184,000—$230,000 USD To ensure the safety of patients and staff, many of our clients require post-offer health screenings and proof and/or completion of various vaccinations such as the flu shot, Tdap, COVID-19, etc. Any requests to be exempted from these requirements will be reviewed by Datavant Human Resources and determined on a case-by-case basis. Depending on the state in which you will be working, exemptions may be available on the basis of disability, medical contraindications to the vaccine or any of its components, pregnancy or pregnancy-related medical conditions, and/or religion. This job is not eligible for employment sponsorship. Datavant is committed to a work environment free from job discrimination. We are proud to be an Equal Employment Opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status. To learn more about our commitment, please review our EEO Commitment Statement here. Know Your Rights, explore the resources available through the EEOC for more information regarding your legal rights and protections. In addition, Datavant does not and will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay. At the end of this application, you will find a set of voluntary demographic questions. If you choose to respond, your answers will be anonymous and will help us identify areas for improvement in our recruitment process. (We can only see aggregate responses, not individual ones. In fact, we aren't even able to see whether you've responded.) Responding is entirely optional and will not affect your application or hiring process in any way. Datavant is committed to working with and providing reasonable accommodations to individuals with physical and mental disabilities. If you need an accommodation while seeking employment, please request it here, by selecting the 'Interview Accommodation Request' category. 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Job Description Position: Director, Commercial Credit Review Department: Commercial Credit Review Reports To: Chief Risk Officer Employment Status: Full-Time, Exempt Office Location: Hybrid, NYC or Miami, FL Supervise: Senior Credit Reviewer Position Summary: The Director of Commercial Credit Review leads the Bank's independent credit review function for the commercial loan portfolio, which includes a balanced mix of commercial real estate (CRE) and a variety of specialty credits. Reporting to the Chief Risk Officer (CRO) with a dotted reporting line to the Risk Committee of the Board of Directors, the Director provides timely, objective, and risk-focused assessments of credit quality, underwriting practices, risk rating accuracy, portfolio concentrations, and compliance with credit policy and regulatory expectations. In addition to leading traditional credit review activities, the Director will champion the responsible adoption of artificial intelligence (AI) and automation across certain aspects of the credit review function. Working with the CRO, Chief Credit Officer, ERM, Information Security, Internal Audit, and IT, the Director will identify, pilot, and scale AI use cases that improve sample coverage, accelerate turnaround time, sharpen exception identification, and strengthen the Bank's overall view of credit risk — within a sound governance framework consistent with regulatory guidance. Essential Responsibilities and Duties: Independent Credit Review Program • Design, maintain, and execute an annual risk-based credit review plan covering commercial loans, including CRE and a variety of specialty credits., • Establish and refresh review scope, sample selection methodology, and coverage thresholds appropriate to a $5–10 billion institution, with documented rationale aligned to the Interagency Guidance on Credit Risk Review Systems., • Deliver clear, timely, and well-supported written reports to executive management, the CRO, and the Board's Risk Committee, with actionable findings and tracked management responses., • Maintain the credit review charter, policies, and procedures, ensuring the function's independence.Loan-Level File Review, • Lead targeted loan-level reviews assessing underwriting quality, financial analysis, collateral evaluation, structure, documentation, covenant monitoring, and ongoing servicing., • Evaluate adherence to the Bank's credit policy, regulatory requirements, and prudent lending standards., • Identify documentation, exception, and policy-deviation trends and escalate root-cause issues to the CRO, CCO and Board Risk Committee.Risk Rating Accuracy & Validation, • Independently validate the accuracy and consistency of risk ratings (pass and criticized) across the commercial portfolio and assess timeliness of rating changes., • Assess the calibration and discriminatory power of the Bank's risk rating framework; recommend enhancements to definitions, factors, and override governance.Policy & Underwriting Compliance, • Evaluate the design and operating effectiveness of the Bank's commercial credit policies, underwriting standards, exception processes, and approval authorities., • Identify gaps between policy, practice, and regulatory expectations; recommend enhancements to policy, procedure, and training., • Coordinate with Internal Audit, Compliance, and Loan Operations to ensure issues are identified appropriately and that root causes are addressed.AI & Automation Strategy for Credit Review, • Serve as the credit review function's champion for responsible AI adoption — building the business case, prioritizing high-value use cases, and partnering with stakeholders to pilot and scale solutions., • Identify candidate use cases such as automated document intake and spreading, covenant and financial-reporting tracking, exception detection, anomaly identification in risk ratings, expanded sampling, and natural-language summarization of credit files., • Partner with ERM, Information Security, Legal, Compliance, and IT to ensure AI tools are deployed within a robust governance framework consistent with regulatory expectations and industry best practices., • Establish controls for explainability, human-in-the-loop review, data lineage, bias and drift monitoring, and audit defensibility for any AI tools deployed in credit review., • Develop metrics that measure the impact of AI on coverage, cycle time, exception identification rates, and finding quality — and share results transparently with the CRO, executive management, and the Board.Leadership & Stakeholder Engagement, • Lead, develop, and mentor a small team of 1–3 credit review professionals; maintain a culture of objectivity, intellectual honesty, and continuous improvement., • Build constructive working relationships with the CRO, CCO, Chief Audit Executive, CFO, lending leadership, and external auditors and regulators., • Represent credit review in regulatory examinations (FDIC/OFR, as applicable) and external audits; serve as a primary point of contact for credit review-related findings., • Bachelor's degree in finance, accounting, business, economics or related field. Master's degree preferred., • CFA, FRM, CPA, CRC or formal credit training preferred., • 15+ years of progressive commercial banking experience, with at least 7 years in credit review, credit risk, commercial underwriting, special assets, or a combination, including (preferably) time at a bank with similar size, complexity, and regulatory profile., • Demonstrated mastery of commercial credit analysis, structuring, and risk rating frameworks., • Experience participating substantively in regulatory examinations and external audits of commercial credit functions., • Track record of producing clear, evidence-based written reports and presenting to executive management and a Board-level committee., • Genuine willingness to champion AI and automation in credit review, balanced with an instinct for sound governance, model risk, and audit defensibility., • Strong leadership, coaching, and people management skills., • Independence and intellectual honesty — willing to deliver hard messages with evidence and tact., • Strategic mindset paired with hands-on attention to detail., • Strong written and verbal communication, including Board-level presentation skills., • Curiosity and adaptability — a real interest in how AI and modern tooling can responsibly improve credit risk oversight., • Collaborative influence across first, second, and third lines of defenseNew York pay range$250,000—$275,000 USD

Job DescriptionAbout Fusemachines Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.Salary Range: US$ 140,000-190,000/yearRole Overview We’re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You’ll work across the ML lifecycle—from problem framing and data exploration to model development, evaluation, deployment, and monitoring—often in partnership with client stakeholders and internal delivery teams. You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems. Key Responsibilities • Problem Framing & Stakeholder Partnership, • Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)., • Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)., • Data Analysis & Feature Engineering, • Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses., • Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices., • Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions., • Model Development (Core ML), • Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data)., • Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation., • Build time series models (statistical and ML/DL approaches) and validate with proper backtesting., • Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness., • Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making., • Deep Learning, • Build and train deep learning models using PyTorch or TensorFlow/Keras., • Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)., • Evaluation, Explainability, and Iteration, • Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports., • Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence., • Productionization & MLOps (Project-Dependent), • Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration., • Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans., • Documentation & Communication, • Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders., • Create documentation and lightweight demos that make results actionable.Success in This Role Looks Like, • You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)., • Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring., • Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly.Required Qualifications, • 3–8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)., • Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)., • Strong SQL skills (joins, window functions, aggregation, performance awareness)., • Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset., • Hands-on experience across multiple model types, including:, • Classification & regression, • Time series forecasting, • Clustering/segmentation, • Experience with deep learning in PyTorch or TensorFlow/Keras., • Strong problem-solving skills: ability to work with ambiguous goals and messy data., • Clear communication skills and ability to translate analysis into decisions.Preferred Qualifications, • Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)., • Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)., • Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)., • Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)., • Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)., • Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks), • Databricks certifications (Data Scientist, Data Engineer, or related)Nice-to-Have, • Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)., • Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products., • Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability.Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws. Important: Immigration Sponsorship Policy Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including: • Direct Company Sponsorship: Such as H-1B, J-1, or TN visas., • Employer of Record: Listing Fusemachines as the immigration employer on any government documentation. I0GDC3XJwj

Job DescriptionAbout Fusemachines Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.Salary Range: US$ 140,000-170,000/year Note: We are not considering OTP STEM visas neither H1B support for this roleRole Overview We’re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You’ll work across the ML lifecycle—from problem framing and data exploration to model development, evaluation, deployment, and monitoring—often in partnership with client stakeholders and internal delivery teams. You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems. Key Responsibilities • Problem Framing & Stakeholder Partnership, • Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)., • Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)., • Data Analysis & Feature Engineering, • Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses., • Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices., • Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions., • Model Development (Core ML), • Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data)., • Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation., • Build time series models (statistical and ML/DL approaches) and validate with proper backtesting., • Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness., • Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making., • Deep Learning, • Build and train deep learning models using PyTorch or TensorFlow/Keras., • Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)., • Evaluation, Explainability, and Iteration, • Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports., • Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence., • Productionization & MLOps (Project-Dependent), • Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration., • Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans., • Documentation & Communication, • Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders., • Create documentation and lightweight demos that make results actionable.Success in This Role Looks Like, • You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)., • Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring., • Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly.Required Qualifications, • 3–8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)., • Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)., • Strong SQL skills (joins, window functions, aggregation, performance awareness)., • Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset., • Hands-on experience across multiple model types, including:, • Classification & regression, • Time series forecasting, • Clustering/segmentation, • Experience with deep learning in PyTorch or TensorFlow/Keras., • Strong problem-solving skills: ability to work with ambiguous goals and messy data., • Clear communication skills and ability to translate analysis into decisions.Preferred Qualifications, • Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)., • Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)., • Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)., • Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)., • Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)., • Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks), • Databricks certifications (Data Scientist, Data Engineer, or related)Nice-to-Have, • Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)., • Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products., • Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability.Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws. Important: Immigration Sponsorship Policy Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including: • Direct Company Sponsorship: Such as H-1B, J-1, or TN visas., • Employer of Record: Listing Fusemachines as the immigration employer on any government documentation. pYO5KHk85I

Job Description StubHub is on a mission to redefine the live event experience on a global scale. Whether someone is looking to attend their first event or their hundredth, we're here to delight them all the way from the moment they start looking for a ticket until they step through the gate. The same goes for our sellers. From fans selling a single ticket to the promoters of a worldwide stadium tour, we want StubHub to be the safest, most convenient way to offer a ticket to the millions of fans who browse our platform around the world. The Role We're seeking a Senior Data Scientist to lead the science behind a best-in-class ticketing experience for fans, with a focus on the operational journey from ticket purchase through event entry. In this role, you will own and develop predictive and decisioning models that proactively identify and mitigate fulfillment issues, strengthen inventory validation, and optimize customer service policies — all while balancing fan experience with operational constraints. You will work on problems where getting it right matters: predicting issues before they impact fans, identifying emerging pain points at scale, and turning moments of potential failure into opportunities to build trust and loyalty. This includes building and productionizing models on both structured and unstructured data, such as LLM-backed pipelines that extract actionable signals from customer communications and support interactions. You will partner closely with product managers, analytics engineers, operations leaders, and other data scientists to translate ambiguous business problems into durable, production-ready data products, and to influence decisions that measurably improve the fan experience. Location: Hybrid (3 days in office/2 days remote) – New York, NY or Century City, CA What You'll Do • Own the science behind ticket fulfillment: Drive reliability, ease of use, and customer satisfaction by crafting and deploying scalable, production-grade machine learning models that directly inform operational systems and decisions., • Drive data-informed strategy: Work with operations partners to shape roadmaps, using analysis and modeling to identify, prioritize, and size high-impact opportunities that can be implemented and sustained at scale., • Design for validation: Apply principles of experimentation and causal inference to ensure work products can be rigorously evaluated, defining offline validation and online testing strategies early in the design process., • Build the data foundation: Partner with platform and analytics teams to ensure data availability and quality, including event instrumentation, batch and streaming ETL, feature stores, and monitoring for model performance, bias, and drift., • 5+ years of industry experience in data science or machine learning, with an MS or PhD in mathematics, statistics, computer science, or a related quantitative field preferred., • Strong programming skills in Python, with experience using numerical and machine learning libraries such as Pandas, NumPy, SciPy, scikit-learn, and gradient-boosting frameworks (e.g., LightGBM, XGBoost)., • Demonstrated experience building, deploying, and iterating on production machine learning models in cloud environments (e.g., AWS, Azure), including post-deployment monitoring and improvement., • Expertise working with large-scale data using modern analytics and compute platforms such as Snowflake, BigQuery, or Databricks, with strong proficiency in SQL., • Proven ability to establish data science methodology in new or ambiguous domains, owning work end to end—from stakeholder alignment and problem framing to delivery and measurable impact., • Experience applying machine learning to operational problem spaces such as ticket fulfillment, fraud detection, customer service, or trust and safety., • Experience with production ML platforms and workflows, such as Metaflow or comparable systems (e.g., Airflow-based ML pipelines, Kubeflow, SageMaker Pipelines, Prefect, Flyte), including model versioning, scheduling, and monitoring., • Accelerated Growth Environment: An environment designed for swift skill and knowledge enhancement, where you have the autonomy to lead experiments and tests on a massive scale., • Top Tier Compensation Package: Competitive base, equity, and upside that tracks with your impact., • Flexible Time Off: Enjoy unlimited Flex Time Off, giving you the flexibility to manage your schedule and take time to recharge as needed.

Job Description Job Duties: Performs and interprets automated and manual testing procedures on blood, urine and fluid specimens in the laboratory. Evaluates specimen/test orders for acceptance/rejection according to guidelines; accessions specimens accurately. Meets established TAT and productivity standards. Recognizes and resolves errors/discrepancies or problems and notifies appropriate personnel. Is proficient with all IT systems; can order, cancel, retrieve result, review work lists, sample collections, and test status. Addresses critical results and delivers STAT results properly according to policy. Correlates results with diagnosis and compares previous results if necessary. Can operate all Laboratory testing instruments; can recognize, troubleshoot and resolve instrument issues. Ensures proper function, maintenance and QC of each instrument; ensures compliance with QC, maintenance, temperature and reagent records. Performs proficiency testing and quality audits as needed. Ensures compliance with JCAHO, FDA, CAP, AABB and NJDOH. Maintain the record of reagents and supply; reports reagents shortage to the supervisor or designee; ensures that all reagents used for testing are in date and show no signs of drift or contamination as indicated in the daily QC data. Assists with training new employees, participates in teaching students. Demonstrates knowledge and follows to all department safety procedures and policies. Ensures proper handling of chemical and bio-hazard materials. Works weekdays, weekends and holidays as scheduled. Conducts personal business on non-productive time, limits socializing to ensure the work flow continues uninterrupted. Shows leadership quality; assumes responsibility in absence of supervisor. Attends department meetings and participates in continue education. EDUCATION + EXPERIENCE REQUIREMENTS: BS degree from an accredited institution with a major in medical technology. Minimum of 1 year clinical laboratory experience, which may be concurrent or subsequent to education, required. MT (ASCP) or equivalent certification desired. May be required to meet specific state requirements. CREDENTIALS + QUALIFICATIONS: Degrees from foreign institutions will require credential equivalency evaluation. Ability to provide excellent customer service internally and externally. Possess superior written and verbal communication skills. Ability to read, understand and follow detailed procedures. Analytical, numerical and reasoning abilities necessary. Proven skills of critical thinking, problem solving, and decision making. Ability to prioritize, organize, and handle a high volume workload efficiently essential. Good close vision and color perception required.

Job Description Senior Software Engineer (Agentic AI / Healthcare) Location: New York, NY (Manhattan), Hybrid (3 days onsite) Compensation: $150,000–$250,000 base + equity Contact / Apply: Ryan Johnston - About Our Client Our client is a venture-backed healthcare technology company based in New York and Toronto, building agentic AI solutions that reimagine how medications are prescribed, verified, and supported across the pharmacy ecosystem. Backed by $4M in funding and trusted by multiple Fortune 50 healthcare organizations, they're scaling quickly and expanding their AI product line. As an early engineering hire, you'll play a meaningful role in shaping architecture, shipping production systems, and balancing speed with quality. The work directly impacts pharmacy workflows and, most importantly, patient outcomes. Mission Our client is building a world where medication delays are impossible. From the moment a prescription is written, the path to therapy start should be clear, fast, and predictable. Their platform connects the fragmented steps that slow medication access, intake, coordination, utilization management, and clinical services, so patients start therapy sooner. The Role Our client is hiring a Senior Software Engineer to design and build autonomous, agentic systems that run complex, multi-step pharmaceutical workflows end-to-end. This is a backend-leaning role with broad scope: you'll work across orchestration, integrations, reliability/observability, and the practical realities of healthcare systems. This role is ideal for someone who enjoys ambiguous problems, can ship end-to-end, and wants to build real product in a regulated, high-stakes domain. What You'll Do * Design and build autonomous agents that handle multi-step pharmacy workflows end-to-end * Architect communication systems that coordinate across organizational boundaries (providers, payers, pharmacies, vendors) * Build orchestration layers for long-running tasks, including graceful failure handling and human-in-the-loop escalation * Develop frameworks enabling agents to interact with external pharmacy systems, databases, and APIs * Implement evaluation + observability infrastructure to monitor agent behavior in production (quality, drift, failure modes) * Integrate with healthcare data standards and legacy pharmacy systems * Scale systems for real-time processing across multiple pharmacy networks What Our Client Is Looking For Must-Have Qualifications * 5+ years of software engineering experience with a strong backend focus * Proficiency in Python or TypeScript and deep backend fundamentals (APIs, data stores, concurrency, distributed systems) * Experience with service-oriented architectures and web frameworks (FastAPI, Flask, or equivalent) * Strong API design skills (clean, versioned APIs) * Familiarity with caching, messaging, and databases (e.g., Redis, Kafka, SQL/NoSQL, vector databases) and how to apply them for performance and reliability * Cloud experience (Azure, AWS, or GCP) * Experience building and deploying agentic AI systems, including tool use, planning, memory, and orchestration patterns * Proven ability to ship ambiguous projects end-to-end with minimal guidance and own outcomes * Based in NYC and able to commute to Manhattan 3 days/week Nice-to-Haves * Experience integrating with healthcare/pharmacy systems or healthcare data standards * Experience building evaluation harnesses for agent reliability and hallucination mitigation * Prior work in regulated environments where correctness and auditability matter Compensation & Perks * Competitive compensation: $150k–$250k base + stock options * Full medical, dental, vision coverage * Unlimited PTO + 2 weeks company-wide holiday closure * Complimentary breakfast daily and weekly team lunches (in-office) * Professional development stipend Why This Role This is a rare opportunity to be early, own architecture, and build agentic systems that directly improve medication access—where execution quality matters and the impact is tangible.

Job DescriptionA little about us… Knit is the AI-native consumer research platform helping brands automate and accelerate primary research. With our Researcher-Driven AI, we've condensed the entire quant + qual research process from weeks into days (sometimes hours!) for 50+ enterprise brands — including Amazon, T-Mobile, Mars, NASCAR, and more. We're on a mission to scale and democratize world-class research. From survey generation to stakeholder-ready reports, our platform is redefining how insights teams operate — and we need your help to push the limits of what's possible. Overview We're continuing to build out our NYC engineering team and are looking for exceptional builders who are resourceful, deeply technical, and thrive in high-ownership environments. As an early member of the team, you'll play a key role in shaping the core of our platform and contributing to a team that will scale to 20+ engineers over the next 18 months. You'll build and orchestrate AI agents, explore new UX paradigms, and work on some of the hardest problems in applied AI with meaningful ownership across the stack. Responsibilities | What you will own... Primary responsibilities of this role: • Own 0→1 and 1→N features across the stack, from design docs to building high-throughput data pipelines and scalable FastAPI services., • Architect and own the core agent orchestration engine, designing complex, multi‑agent LLM workflows (routing, tool‑use, memory, evaluation) with deterministic fallbacks, guardrails, and auditable actions required by enterprise users., • Build AI‑native, human‑in‑the‑loop UIs. Craft React/Next.js interfaces that let users collaborate safely with agents, with transparency and explainability by design., • Develop the "factory for the factory". Build our experimentation platform: CI/CD for prompts and agents, offline/online evals, drift detection, safety layers, and rollout controls., • Ship fast, safely. You'll instrument everything for reliability, write concise design docs, add robust tests, and build dashboards and alerts for features you launch., • Partner cross‑functionally with Product, AI, Design, CS; talk to customers and SMEs, and turn ambiguous problems into shipped product., • Obsess over performance, optimizing for latency across the system: caching, model selection, distillation, batching, vector search, and infrastructure efficiency (GPU/CPU)., • Operate what you build. This includes on‑call ownership, leading incident response, and driving learning through blameless postmortems., • Level-up the team around you. Mentor and collaborate with other engineers to help make the entire team better.This role is for you if..., • You're a 0 → 1 builder who thrives in ambiguity and has shipped products from scratch at a startup or in a hyper-growth phase. You've seen both - early-stage hustle and later-stage scale and know how to build for it., • You're a systems thinker with deep technical chops: you design clear architectures and write clean, tested Python and TypeScript., • You think like an owner: you spot problems, propose solutions, and build them without waiting for perfect specs. Your impact is measured in business outcomes., • You use AI as leverage. Tools like Cursor, Copilot, or Claude Code are integral to your workflow, helping you ship faster while maintaining a high standard for correctness., • You believe speed and quality aren't opposites and can articulate the tradeoffs you make., • You communicate crisply. You share your ideas effectively and thrive in tight feedback loops with product, design, and customers., • You've got grit: hard problems energize you, context switching doesn't scare you, and you're comfortable deleting code.Our Stack, • Frontend: React, Next.js, • Backend: Python, FastAPI, • Data/ML: MongoDB, S3, Redis; Pinecone, • Infra: AWS, Docker, Kubernetes; CI/CD with GitHub Actions., • Messaging: SQS; event‑driven pipelines.Required Skills & Experiences The successful candidate will be a high-ownership, product-minded engineer who combines strong full-stack fundamentals with experience building AI-native systems, capable of turning ambiguous problems into scalable, reliable features that drive real business impact. Required: • Experience with LLM/agent systems, evals, and prompt tooling; RAG, embeddings, vector dbs., • Deep knowledge of event‑driven systems, streaming, and workflow engines., • Expertise in observability (OpenTelemetry/Prometheus/Datadog), feature flagging, or A/B platforms., • Background in enterprise security and compliance (SOC 2, GDPR), • Bias to action: short specs, small PRs, daily deploys., • In-person collaboration: we're building the NYC hub together; whiteboards > long threads., • Are 1% better every day: We approach situations with a growth mindset and ask, "How can we make the business better?" and "What would it take?", • Play to win: We set audacious goals and push ourselves to achieve them with a bias towards action (When we see a need, we take initiative, and hold ourselves accountable to seeing it through). CCPA disclosure here.

Job Description Capgemini is at the forefront of Generative AI innovation, helping Financial Services clients industrialize GenAI and Agentic AI platforms at enterprise scale. We are seeking an experienced and innovative AI Product Engineer – Agentic Platforms to join our Financial Services Artificial Intelligence & Business Lines (FS‑ABL) practice. This role is ideal for a consulting technologist with deep expertise in modern GenAI tooling, agentic system design, and enterprise SDLC, who can partner directly with clients to envision, design, develop, and deploy Agentic AI platforms in regulated environments. In this role, you will work at the intersection of client advisory, AI product engineering, and delivery execution, helping banks, insurers, and capital markets firms transition from GenAI pilots to production‑grade, governed, multi‑agent systems. You will apply leading GenAI frameworks and LLM platforms — including Anthropic, OpenAI, LangChain, LangGraph, DSPy, and vector databases—while operating across the full Agentic SDLC. P&C Insurance knowledge and experience is a significant plus. Additionally, familiarity with core insurance platforms like Guidewire, DuckCreek or Majesco will be extremely helpful to succeed in this role. We are looking for candidates across all levels of experience and expertise - junior through senior level AI Product Engineers. Requirements Client Advisory & Product Vision • Partner directly with Financial Services clients to identify, prioritize, and shape Agentic AI use cases across customer operations, underwriting, claims, risk, compliance, finance, and technology., • Lead client workshops to define agent personas, responsibilities, autonomy boundaries, human‑in‑the‑loop checkpoints, and escalation logic., • Translate evolving business needs into agentic product backlogs, roadmaps, and MVP definitions., • Design and implement multi‑agent architectures using modern GenAI tooling, including:, • Planner, executor, reviewer/critic, and supervisor agents, • Tool‑calling and function‑calling agents, • Memory‑enabled agents (conversation, semantic, episodic, and structured memory), • Leverage LangChain and LangGraph for agent orchestration, workflows, and control flow., • Apply DSPy and declarative prompt optimization techniques for repeatability, performance tuning, and regression control., • Design agent interaction patterns such as hierarchical agents, collaborating agents, and event‑driven agent workflows., • Own delivery across the full Software Development Lifecycle (SDLC), extending it into a formal Agentic SDLC, including:, • Agent design specifications and behavior contracts, • Prompt, policy, and tool versioning, • Simulation environments and offline evaluation, • Automated testing of agent flows and guardrails, • Controlled rollout, telemetry‑driven optimization, and continuous learning, • Build production‑grade AI services primarily using Python, integrating:, • LLM providers such as Anthropic (Claude), OpenAI, and open‑source models, • Retrieval‑Augmented Generation (RAG) using vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate), • Implement CI/CD pipelines for agent code, prompts, and policies., • Implement agent observability including tracing, decision logging, tool usage, and failure analysis., • Apply evaluation frameworks for hallucination detection, consistency checks, and fitness scoring., • Design feedback loops incorporating human‑in‑the‑loop review and reinforcement., • Design Agentic AI platforms aligned with Financial Services regulatory expectations, including:, • Auditability and traceability of agent decisions, • Model and prompt explainability, • Data privacy and security controls, • Resilience and fail‑safe mechanisms, • Embed guardrails and policies addressing hallucination risk, bias, unauthorized actions, and escalation failures., • Provide technical leadership and mentorship to consulting delivery teams., • Contribute to internal GenAI accelerators, agent frameworks, and reusable assets., • Support RFPs, proposals, and client solution designs with credible GenAI and agentic architectures. This position comes with competitive compensation and benefits package: • Competitive salary and performance-based bonuses, • Comprehensive benefits package, • Career development and training opportunities, • Flexible work arrangements (remote and/or office-based), • Dynamic and inclusive work culture within a globally known group, • Private Health Insurance, • Retirement Benefits, • Paid Time Off, • Training & Development, • *Note: Benefits differ based on employee level About Capgemini Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of over 420,000 team members in more than 50 countries. With its strong 55-year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fueled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms. The Group €22.5 billion in revenues in 2025.

Job DescriptionSenior Network & Security Engineer Location: New York Tri-State Area (NYC / Northern NJ / Fairfield–Westchester CT) Work Model: Hybrid — 3 days on-site at primary data-center campus in Midtown Manhattan or Secaucus, NJ; 2 days WFH. Clearance: None required, but ability to pass a Tier 2 public-trust background check is a must. Travel: < 10 % (regional PoP / DR-site visits)About Us AITHERAS, LLC is a customer-focused IT consulting firm delivering cost-effective, mission-critical solutions since 2002. We specialize in Data Analytics, Cloud Computing, IT Engineering, Application Development, and Cyber Security. Based in Rockville, MD, we’re ISO 9001:2015 certified, an SBA-designated Small Business, and an MBE-certified firm by MDOT. We partner with over 100 clients to create scalable, innovative solutions that drive success.What You’ll Do% TimeResponsibility40 %Design & Implementation – Build and migrate segmentation zones, VRFs, and BGP/OSPF fabrics across Cisco Nexus 9K, Juniper QFX/MX, and Palo Alto PA-Series.25 %Security & Visibility – Stand up TACACS+/Cisco ISE for AAA, integrate flow telemetry into Cisco Secure Network Analytics (StealthWatch), deploy taps/SPANs/Gigamon, and tune IDS/IPS policies for OT & IT.15 %Automation – Write and maintain Python/Ansible/Terraform pipelines (pyATS, Nornir, Jinja2) for golden-config generation, compliance drift detection, and CI/CD-based push-button rollouts.10 %Operations & Incident Response – Serve as L3 escalation and join a 1-week-per-6 on-call rotation; own post-mortems and MTTR metrics.10 %Documentation & Mentoring – Produce HLDs/LLDs, MOPs, and runbooks; coach junior engineers toward CCNP/PCNSE.Must-Have Qualifications • 8+ years progressive experience in enterprise or service-provider networking., • Deep expertise configuring and troubleshooting BGP, OSPF, static routing, VRFs, VXLAN-EVPN., • Hands-on with Cisco Catalyst/Nexus & ASA/FTD, Juniper EX / QFX / MX, and Palo Alto NGFWs (Pan-OS 9/10)., • Solid command of AAA (TACACS+, RADIUS) and production deployments of Cisco ISE or equivalent., • Practical experience deploying or operating flow-analytics / tap infrastructure (StealthWatch, Gigamon, NetFlow/IPFIX, SPANs)., • Comfort writing Python and Ansible playbooks; git-based workflows (GitLab/GitHub, CI pipelines)., • Familiarity with network IDS/IPS (Snort, Zeek, Palo Alto Threat Prevention) and SIEM workflows (Splunk, QRadar)., • Ability to create HLD/LLD diagrams in Visio or draw.io and present them to technical & business leadership., • U.S. work authorization and ability to commute to NYC area 3× week.Nice to Have, • CCNP Enterprise/Security, PCNSE, JNCIS-ENT/Sec, or CCIE (R&S / Enterprise)., • Cisco Secure Network Analytics design experience (flow collector sizing, policy tuning)., • Prior exposure to industrial / OT networks (NIST 800-82, Purdue Model, SCADA segmentation)., • AWS or Azure Advanced Networking certification; Transit Gateway design experience., • Experience with F5 LTM/GTM or Citrix ADC in low-latency trading or fintech environments., • Source-of-truth & intent platforms (NetBox, Nautobot), RESTful API development, or Go/Rust scripting.What We Offer, • Base Salary: FTE: 135K-140K or $90-$100 C2C/1099, • FTE Benefits: 100 % employer-paid medical / dental / vision, 401(k) 5 % match, ESPP, • FTE Training: $3 k annual cert stipend, paid time for labs & conferences (Cisco Live, Palo Alto Ignite) Powered by JazzHR yiMixFkdvZ

Job DescriptionPosition: Lead Software Developer | Trading & Tax Lot Harvesting (Agentic AI, .NET, AWS, Angular)Location: Fort Mill, SC | New York, NY | Austin, TXExperience: 10–12+ yearsCompany OverviewIncedo is a US‑based consulting, data science, and technology services firm with more than 3,000 professionals across the US, Mexico, and India. We help clients achieve competitive advantage through end‑to‑end digital transformation, combining deep engineering, data science, and design capabilities with strong domain expertise. Our teams deliver measurable business impact across telecom, banking, wealth management, product engineering, and life sciences & healthcare.Life at IncedoAt Incedo, you’ll work with industry‑leading clients and collaborate with experts in technology and domain strategy. Our learning platform, Incedo University, supports your growth from day one with structured onboarding, continuous learning programs, and clear career pathways. Whether you aspire to become a program manager, technical architect, or domain specialist, we provide the flexibility and support to help you grow. We also foster a collaborative, engaging workplace with regular team events and a friendly environment.Our MissionWe enable our clients to maximize business impact from technology by: • Harnessing the transformational power of emerging technologies, • Bridging the gap between business and engineering Role OverviewWe are seeking a hands-on Technical Lead to build an intelligent Tax Lot Harvesting capability within a trading platform. This role will lead the design of AI-powered decisioning features and ensure seamless integration with Angular-based UI to deliver intuitive, insight-driven user experiences for advisors and traders.Key Responsibilities, • Lead end-to-end design and delivery of Tax Lot Harvesting capabilities, • Architect scalable solutions using .NET, AWS, and Angular-based front-end integration, • Define interaction patterns between agentic AI services and Angular UI (APIs, real-time insights, UX flows), • Enable natural-language-driven insights surfaced through intuitive UI components, • Design systems to identify tax optimization opportunities (gain/loss harvesting, drift, cash), • Ensure seamless embedding of intelligence into Order Entry workflows and UI, • Drive engineering best practices across backend and frontend integration, • Mentor team members across full-stack (backend + Angular) developmentRequired Qualifications, • 10+ years of software development experience, • 7+ years of strong experience in .NET (C#, APIs, microservices), • Working knowledge of Angular (UI integration, API consumption, component design), • Strong experience in trading systems (Order Entry, portfolio workflows), • Proven experience with agentic AI / AI-driven applications, • Strong AWS experience (must-have), • Understanding of tax lot accounting or portfolio tax optimization concepts, • Excellent communication and stakeholder management skillsPreferred Qualifications, • AWS Certified Solutions Architect / Developer, • Experience designing data-driven UI/UX for analytics or decision-support systems, • Familiarity with real-time UI updates (WebSockets, streaming data), • Experience with financial advisory or wealth platforms

Job DescriptionPosition: Senior Software Developer | Trading & Tax Lot Harvesting (Agentic AI, .NET, AWS, Angular)Location: Fort Mill, SC | New York, NY | Austin, TXExperience: 5-8+ YearsCompany OverviewIncedo is a US‑based consulting, data science, and technology services firm with more than 3,000 professionals across the US, Mexico, and India. We help clients achieve competitive advantage through end‑to‑end digital transformation, combining deep engineering, data science, and design capabilities with strong domain expertise. Our teams deliver measurable business impact across telecom, banking, wealth management, product engineering, and life sciences & healthcare.Life at IncedoAt Incedo, you’ll work with industry‑leading clients and collaborate with experts in technology and domain strategy. Our learning platform, Incedo University, supports your growth from day one with structured onboarding, continuous learning programs, and clear career pathways. Whether you aspire to become a program manager, technical architect, or domain specialist, we provide the flexibility and support to help you grow. We also foster a collaborative, engaging workplace with regular team events and a friendly environment.Our MissionWe enable our clients to maximize business impact from technology by: • Harnessing the transformational power of emerging technologies, • Bridging the gap between business and engineering Role OverviewWe are looking for a Senior Developer to build backend services and contribute to Angular-based UI integration for an AI-powered Tax Lot Harvesting platform. You will help deliver intelligent, user-facing features that enable tax-aware trading decisions.Key Responsibilities, • Develop backend services for Tax Lot Harvesting and trading workflows using .NET, • Contribute to Angular UI development for displaying tax insights and recommendations, • Build APIs to support real-time and on-demand UI interactions, • Implement logic for tax optimization, lot selection, and drift analysis, • Integrate agentic AI outputs into UI and backend workflows, • Work with AWS services to build scalable applications, • Write high-quality, testable code (TDD)Required Qualifications, • 5+ years of software development experience, • Strong experience with .NET (C#, APIs, microservices), • Hands-on experience with Angular (components, services, API integration), • Experience in trading or financial systems, • Exposure to AI/ML or agentic AI-based applications, • Hands-on AWS experience (must-have)Preferred Qualifications, • Understanding of tax lot accounting or capital markets workflows, • Experience with data visualization in Angular (charts, dashboards), • Familiarity with real-time data handling, • AWS certification, • Comfort working in a fast-paced, product-driven environment

Job DescriptionImmediate Start - Quant Researcher – Systematic Commodities Hedge Fund Moreton Capital Partners is rapidly expanding and seeking a talented Quantitative Researcher to join us in our Mexico City office to sit alongside a world class, international team. This is a high-impact role from day one. You'll take full ownership of designing, testing, and refining the next generation of alpha signals in commodity futures, with your models feeding directly into live trading portfolios. Our research is grounded in advanced machine learning, robust testing frameworks, and deep expertise across global commodity markets. We're looking for someone ready to hit the ground running and available to start immediately. In return, we offer a competitive salary, substantial performance share, comprehensive benefits, incredible work environment and a relocation package to make the move seamless. Key Responsibilities • Research, prototype, and validate systematic trading signals across commodities using advanced ML methods., • Design and implement rigorous backtests with realistic frictions, walk-forward validation, and robust statistical tests., • Engineer and evaluate novel features from prices, fundamentals, positioning, options data, and alternative datasets (e.g., satellite, weather and global commodity cash pricing)., • Blend multiple alpha forecasts into meta-models and portfolio signals, leveraging ensemble and Bayesian methods., • Develop portfolio construction and optimization techniques and analysis tools to be able to enhance performance and track effects on portfolio execution., • Collaborate with developers to transition research into production-ready strategies., • Masters or PhD in either Statistics, Economics, Computer Science., • Strong background in machine learning and statistical modelling (tree-based models, regularization, time-series ML)., • Proficiency in Python (pandas, NumPy, scikit-learn, XGboost, PyTorch/TensorFlow)., • Understanding of time-series forecasting, cross-validation techniques, and avoiding look-ahead bias., • Academic experience in research and proven ability to translate academic work to production code., • Prior exposure to systematic trading or financial modelling., • Ability to design experiments, interpret results, and iterate quickly in a research environment. Bonus points for: • Knowledge of commodities (agriculture, energy, metals) or macro markets., • Experience with feature engineering on non-traditional datasets (options positioning, weather, satellite)., • Experience collaborating in version control environments., • Familiarity with portfolio optimization, risk parity, or Bayesian model averaging., • Direct impact: Your alphas will go live into production portfolios, with real capital behind them., • Research-first culture: We value deep thinking, novel approaches, and systematic rigor., • Close collaboration across a global team., • Career growth: Clear trajectory to senior researcher roles as we scale AUM and expand product lines., • Attractive compensation: Highly competitive base salary and annual bonus that scales as the business grows., • Relocation package to our Mexico City office, along with a competitive benefits offering that includes health and life insurance, a year-end bonus, and generous paid time off., • Positive, inclusive and encouraging work environment.

Job DescriptionA little about us… Knit is the AI-native consumer research platform helping brands automate and accelerate primary research. With our Researcher-Driven AI, we've condensed the entire quant + qual research process from weeks into days (sometimes hours!) for 50+ enterprise brands — including Amazon, T-Mobile, Mars, NASCAR, and more. We're on a mission to scale and democratize world-class research. From survey generation to stakeholder-ready reports, our platform is redefining how insights teams operate — and we need your help to push the limits of what's possible. Overview We're continuing to build out our NYC engineering team and are looking for exceptional builders who are resourceful, deeply technical, and thrive in high-ownership environments. As an early member of the team, you'll play a key role in shaping the core of our platform and contributing to a team that will scale to 20+ engineers over the next 18 months. You'll build and orchestrate AI agents, explore new UX paradigms, and work on some of the hardest problems in applied AI with meaningful ownership across the stack. Responsibilities | What you will own... Primary responsibilities of this role: • Own 0→1 and 1→N features across the stack, from design docs to building high-throughput data pipelines and scalable FastAPI services., • Architect and own the core agent orchestration engine, designing complex, multi‑agent LLM workflows (routing, tool‑use, memory, evaluation) with deterministic fallbacks, guardrails, and auditable actions required by enterprise users., • Build AI‑native, human‑in‑the‑loop UIs. Craft React/Next.js interfaces that let users collaborate safely with agents, with transparency and explainability by design., • Develop the "factory for the factory". Build our experimentation platform: CI/CD for prompts and agents, offline/online evals, drift detection, safety layers, and rollout controls., • Ship fast, safely. You'll instrument everything for reliability, write concise design docs, add robust tests, and build dashboards and alerts for features you launch., • Partner cross‑functionally with Product, AI, Design, CS; talk to customers and SMEs, and turn ambiguous problems into shipped product., • Obsess over performance, optimizing for latency across the system: caching, model selection, distillation, batching, vector search, and infrastructure efficiency (GPU/CPU)., • Operate what you build. This includes on‑call ownership, leading incident response, and driving learning through blameless postmortems.This role is for you if..., • You're a 0 → 1 builder who thrives in ambiguity and has shipped products from scratch at a startup or in a hyper-growth phase. You've seen both - early-stage hustle and later-stage scale and know how to build for it., • You're a systems thinker with deep technical chops: you design clear architectures and write clean, tested Python and TypeScript., • You think like an owner: you spot problems, propose solutions, and build them without waiting for perfect specs. Your impact is measured in business outcomes., • You use AI as leverage. Tools like Cursor, Copilot, or Claude Code are integral to your workflow, helping you ship faster while maintaining a high standard for correctness., • You believe speed and quality aren't opposites and can articulate the tradeoffs you make., • You communicate crisply. You share your ideas effectively and thrive in tight feedback loops with product, design, and customers., • You've got grit: hard problems energize you, context switching doesn't scare you, and you're comfortable deleting code.Our Stack, • Frontend: React, Next.js, • Backend: Python, FastAPI, • Data/ML: MongoDB, S3, Redis; Pinecone, • Infra: AWS, Docker, Kubernetes; CI/CD with GitHub Actions., • Messaging: SQS; event‑driven pipelines.Required Skills & Experiences The successful candidate will be a high-ownership, product-minded engineer who combines strong full-stack fundamentals with experience building AI-native systems, capable of turning ambiguous problems into scalable, reliable features that drive real business impact. Required: • Experience with LLM/agent systems, evals, and prompt tooling; RAG, embeddings, vector dbs., • Deep knowledge of event‑driven systems, streaming, and workflow engines., • Expertise in observability (OpenTelemetry/Prometheus/Datadog), feature flagging, or A/B platforms., • Background in enterprise security and compliance (SOC 2, GDPR), • Bias to action: short specs, small PRs, daily deploys., • In-person collaboration: we're building the NYC hub together; whiteboards > long threads., • Are 1% better every day: We approach situations with a growth mindset and ask, "How can we make the business better?" and "What would it take?", • Play to win: We set audacious goals and push ourselves to achieve them with a bias towards action (When we see a need, we take initiative, and hold ourselves accountable to seeing it through). CCPA disclosure here.

Job Description Job Duties: Performs and interprets automated and manual testing procedures on blood, urine and fluid specimens in the laboratory. Evaluates specimen/test orders for acceptance/rejection according to guidelines; accessions specimens accurately. Meets established TAT and productivity standards. Recognizes and resolves errors/discrepancies or problems and notifies appropriate personnel. Is proficient with all IT systems; can order, cancel, retrieve result, review work lists, sample collections, and test status. Addresses critical results and delivers STAT results properly according to policy. Correlates results with diagnosis and compares previous results if necessary. Can operate all Laboratory testing instruments; can recognize, troubleshoot and resolve instrument issues. Ensures proper function, maintenance and QC of each instrument; ensures compliance with QC, maintenance, temperature and reagent records. Performs proficiency testing and quality audits as needed. Ensures compliance with JCAHO, FDA, CAP, AABB and NJDOH. Maintain the record of reagents and supply; reports reagents shortage to the supervisor or designee; ensures that all reagents used for testing are in date and show no signs of drift or contamination as indicated in the daily QC data. Assists with training new employees, participates in teaching students. Demonstrates knowledge and follows to all department safety procedures and policies. Ensures proper handling of chemical and bio-hazard materials. Works weekdays, weekends and holidays as scheduled. Conducts personal business on non-productive time, limits socializing to ensure the work flow continues uninterrupted. Shows leadership quality; assumes responsibility in absence of supervisor. Attends department meetings and participates in continue education. EDUCATION + EXPERIENCE REQUIREMENTS: BS degree from an accredited institution with a major in medical technology. Minimum of 1 year clinical laboratory experience, which may be concurrent or subsequent to education, required. MT (ASCP) or equivalent certification desired. May be required to meet specific state requirements. CREDENTIALS + QUALIFICATIONS: Degrees from foreign institutions will require credential equivalency evaluation. Ability to provide excellent customer service internally and externally. Possess superior written and verbal communication skills. Ability to read, understand and follow detailed procedures. Analytical, numerical and reasoning abilities necessary. Proven skills of critical thinking, problem solving, and decision making. Ability to prioritize, organize, and handle a high volume workload efficiently essential. Good close vision and color perception required.

Get to know Okta Okta is The World’s Identity Company. We free everyone to safely use any technology, anywhere, on any device or app. Our flexible and neutral products, Okta Platform and Auth0 Platform, provide secure access, authentication, and automation, placing identity at the core of business security and growth. At Okta, we celebrate a variety of perspectives and experiences. We are not looking for someone who checks every single box - we’re looking for lifelong learners and people who can make us better with their unique experiences. Join our team! We’re building a world where Identity belongs to you. The Team We safeguard Okta from threats and vulnerabilities that could disrupt our business or erode our customers’ trust. The Defensive Cyber Engineering organization is responsible for the Security Data Platform, infrastructure, and tooling that enable Okta’s Detection and Response capabilities. The team also manages enterprise security tools and infrastructure that support XDR, DLP, and SSPM capabilities. The Role We are expanding this team to support Okta’s Public Sector roadmap by hiring a Staff Software Engineer in the DC area. You’ll work closely with Security, Business Technology Engineering, and Product teams to implement and manage security solutions, ensuring our core infrastructure applications protect our workforce, endpoints, and corporate data. To deliver on this vision, you will build data and infrastructure platforms by leveraging your software, data, and security engineering skills. You’ll use automation, policy-as-code, and cloud-native technologies to deliver scalable, resilient, and secure solutions. What you bring: • 8+ years of strong coding and scripting experience, with a focus on building automation and custom tooling for infrastructure and data pipelines. Experience with Python is required; proficiency in other languages (e.g., Bash, PowerShell, Go) is a plus., • Experience developing and maintaining ETL/ELT pipelines for onboarding security data into Okta’s security data lake., • Experience working with security teams to integrate log sources and ensure seamless data ingestion., • Ability to build and maintain scalable, fault-tolerant data pipelines to process security event data., • Proven track record of automating security controls and workflows using a cloud-first approach., • Experience with Terraform and other infrastructure-as-code tools to orchestrate security infrastructure., • Familiarity with CI/CD pipelines for security automation and drift management., • Strong communication skills, with the ability to collaborate with technical staff, support teams, executive leadership, and external vendors., • 2+ years of experience building engineering solutions within highly regulated environments such as FedRAMP High, IL4, IL5, and/or IL6 is a plus., • Current or previous Secret, Top Secret (TS), or Top Secret/Sensitive Compartmented Information (TS/SCI) clearance is a plus. What you’ll be doing • Serve as a security subject matter expert (SME) for solution engineering and architecture reviews, preferably in the Detection and Response and/or enterprise security tooling space., • Lead technical efforts to evaluate, design, and implement new enterprise security systems and feature enhancements., • Build, maintain, and enhance custom automation and cloud infrastructure using Terraform or similar tools to support team workflows and enforce security controls., • Develop integrations with APIs, cloud platforms (AWS, GCP, Azure), and security infrastructure to improve detection, response, and remediation., • Write and maintain scripts and automations, with an emphasis on Python-based solutions, to streamline security operations., • Establish monitoring and alerting for security posture, misconfigurations, and threats across endpoints, SaaS, and cloud workloads., • Proactively identify and remediate security gaps; stay updated on emerging threats, solutions, and tooling across the industry. And extra credit if you have experience in any of the following! • Advanced identity management technologies (MFA, SAML, OAuth, OIDC, WebAuthn), • Deep understanding of Okta's ecosystem, including advanced configuration and integrations, • Experience with continuous compliance solutions (e.g., policy-as-code, automated evidence gathering) Additional requirements: • This position requires the ability to access federal environments and/or have access to protected federal data. As a condition of employment for this position, the successful candidate must be able to submit documentation establishing U.S. Person status (e.g. a U.S. Citizen, National, Lawful Permanent Resident, Refugee, or Asylee. 22 CFR 120.15) upon hire. #LI-REMOTE #LI-SH1 P24527_3348076 Below is the annual base salary range for candidates located in California (excluding San Francisco Bay Area), Colorado, Illinois, New York and Washington. Your actual base salary will depend on factors such as your skills, qualifications, experience, and work location. In addition, Okta offers equity (where applicable), bonus, and benefits, including health, dental and vision insurance, 401(k), flexible spending account, and paid leave (including PTO and parental leave) in accordance with our applicable plans and policies. To learn more about our Total Rewards program please visit:___. The annual base salary range for this position for candidates located in California (excluding San Francisco Bay Area), Colorado, Illinois, New York, and Washington is between:$161,000—$221,000 USD What you can look forward to as a Full-Time Okta employee! • Amazing ___, • Making ___, • Developing ___ Okta cultivates a dynamic work environment, providing the best tools, technology and benefits to empower our employees to work productively in a setting that best and uniquely suits their needs. Each organization is unique in the degree of flexibility and mobility in which they work so that all employees are enabled to be their most creative and successful versions of themselves, regardless of where they live. Find your place at Okta______. Some roles may require travel to one of our office locations for in-person onboarding. Okta is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, marital status, age, physical or mental disability, or status as a protected veteran. We also consider for employment qualified applicants with arrest and convictions records, consistent with applicable laws. If reasonable accommodation is needed to complete any part of the job application, interview process, or onboarding please use this___ to request an accommodation. Notice for New York City Applicants & Employees: Okta may use Automated Employment Decision Tools (AEDT), as defined by New York City Local Law 144, that use artificial intelligence, machine learning, or other automated processes to assist in our recruitment and hiring process. In accordance with NYC Local Law 144, if you are an applicant or employee residing in New York City, please ___ to view our full NYC AEDT Notice. Okta is committed to complying with applicable data privacy and security laws and regulations. For more information, please see our Personnel and Job Candidate Privacy Notice at ___

Who we are: Shape a brighter financial future with us. Together with our members, we’re changing the way people think about and interact with personal finance. We’re a next-generation fintech company using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world. The role: We are seeking an experienced, technically strong Home Loans Senior Data Analyst to own end-to-end analytics for our residential mortgage portfolio and translate performance insights into a measurable feedback loop that improves lending strategies. In this role, you will analyze loan performance across the lifecycle (origination → servicing → loss/prepay outcomes), build scalable reporting and monitoring, and partner closely with Underwriting, Credit Policy, Operations, Compliance, Capital Markets, 2LOD and Product to turn data into decisions. You will be responsible for defining the "truth" of portfolio performance, identifying emerging risk and opportunity signals, and driving changes to guidelines, overlays, operational processes, and product strategy then measuring impact. Joining SoFi means you’re part of a company reshaping financial services through technology, combining the speed of a startup with the scale and rigor of an established industry leader. This is an opportunity to directly influence Home Loans credit risk strategy within a high-growth, high-impact team. What you’ll do: • Define and maintain portfolio performance KPIs across credit, profitability, and risk (e.g., delinquency/roll rates, loss rates, severity, prepayment speeds, cure rates, early payment defaults, repurchase risk, defect rates)., • Perform cohort/vintage and segmentation analysis by credit score, LTV/CLTV, DTI, lien position, documentation type, occupancy, channel, state/metro, property type, and investor/product., • Build and maintain executive dashboards and automated reporting that clearly explain what changed, why it changed, and what to do next., • Partner with Data Engineering to define data requirements and create new data sources as required AND/OR create summarized tables as needed, ensuring data structures support scalable analysis and consistent reporting., • Monitor and report portfolio concentration limits and emerging risk exposures (credit attributes, geography, product/investor mixes)., • Develop early warning indicators that proactively identify deteriorating performance (e.g., delinquency migration, geographic stress, valuation drift, policy exception trends)., • Track portfolio risk related to market shifts (home price changes, rate environment, regional decline signals) and quantify impact to credit outcomes., • Order, track, and report refreshed collateral values across the portfolio; analyze LTV migration and identify segments with increasing collateral risk., • Partner with servicing/operations to ensure collateral refresh processes are auditable, timely, and decision-relevant., • Conduct gap analyses across investor guidelines and product types; translate guideline differences into data-backed recommendations for eligibility rules, overlays, and workflow controls., • Review loan defects, exceptions, and deficiencies; perform root-cause analysis and quantify performance impact (loss, repurchase, cycle time, fallout)., • Create a closed-loop learning system:, • Identify drivers of performance (risk and profitability), • Recommend strategy changes (policy, pricing, operational controls, product), • Implement with partners, • Measure results via before/after analysis, monitoring, and clear success metrics, • Partner with Product, Data Engineering, and business stakeholders to deliver analytics solutions (dashboards, metric definitions, datasets, alerts) with strong documentation and governance., • Lead the execution of analytics initiatives using structured project practices (requirements, timelines, stakeholder communication, UAT, rollout)., • Ensure data integrity: metric definitions, reconciliations, QA checks, and consistent reporting across teams., • Portfolio analytics expertise: segmentation, vintage analysis, trend attribution, and performance driver identification., • Decision science mindset: hypothesis-driven analysis, measurement design, and quantifying trade-offs (risk vs. growth vs. profitability)., • Data rigor: reconciliation discipline, metric governance, documentation, and repeatable processes., • Business translation: turning analysis into clear actions for underwriting strategy, policy changes, and operational improvements., • Influence without authority: partnering across Underwriting, Ops, Compliance, Capital Markets, and Product to implement change., • Execution strength: delivering analytics products (dashboards, datasets, alerts) reliably and improving them over time., • Advanced degree (MS/MBA/PhD) in a quantitative field., • Familiarity with modern data stacks (e.g., Snowflake/BigQuery/Redshift, dbt, Airflow) and version control (Git)., • Experience supporting capital markets needs: investor reporting, securitization support, due diligence, credit tape analysis, and guideline overlays., • Experience designing and evaluating policy tests/experiments (A/B testing where applicable, quasi-experimental measurement, monitoring for unintended impacts)., • Tools: JIRA/Confluence, Airtable, and project planning practices for analytics delivery.

$105k-$175k · 0.01%-0.05% Equity We have a product team that understands our users deeply — the agents, the workflows, the edge cases, the moments that matter. They know what needs to be built. What they don't have is a high efficiency path from that understanding to a polished, production-grade frontend experience. That’s what this role solves. You'll lead frontend engineering for the platform by setting the technical direction, owning quality and CI/CD, and building the processes and guardrails that let product-minded people translate domain expertise directly into frontend features. You will also own our "Sandbox" middleware (Express/Postgres) that powers rapid feature iteration along side backend engineering. The goal isn't to centralize frontend development in one person it's to democratize it, putting the build capability in the hands of the people closest to the problem. You bring serious frontend chops. Not to write every feature yourself, but to set a bar that's high enough to matter and build the scaffolding, guardrails, and tooling that pulls everyone else up to it. Technical Leadership & Governance • Architecture Standards: Set and enforce frontend standards, defining the patterns and conventions that make the codebase coherent as it scales., • Component Strategy: Design and maintain a high-integrity component library (Storybook/Shadcn) that serves as the visual source of truth., • Scaffolding: Build the templates, documented workflows, and reusable primitives that allow non-engineers to contribute confidently., • Agentic Guardrails: Design the "safety net" (custom linters, strict TypeScript patterns, and PR checks) that ensures code generated via Replit, Cursor, or ClaudeCode meets production standards., • The "Fast Path": Manage the sandbox stack used for rapid feature testing and validation., • API Development: Own the API layer and schema changes required to ship frontend features independently., • Data Lifecycle & State: Architect sophisticated data-fetching and caching strategies (e.g., TanStack Query) to ensure the UI is always snappy and consistent., • Performance at Scale: Manage global state and distributed caching (e.g., ElastiCache/Redis) to minimize latency and prevent unnecessary database load., • Asynchronous Orchestration: Design the logic for long-running job management and real-time updates, ensuring the UI handles complex backend processes gracefully., • Path to Production: Own the merge process, ensuring nothing hits production without passing the bar you set., • Automated Validation: Implement regression testing and rollback strategies to prevent "UI drift" or broken workflows in a multi-contributor environment., • Environment Management: Manage the transition of features from the Sandbox into the production-grade frontend., • You are a Frontend specialist who is "Full-Stack capable." You love the UI, but you aren't afraid to dive into an Express route or an Aurora schema to get the job done., • You view Developer Experience (DX) as a product. You get genuine satisfaction from building tools that make others faster and more capable., • You are "AI-Native" in your thinking. You aren't threatened by non-technical users using AI agents; you are excited to build the infrastructure that makes that workflow safe and scalable., • You have deep React/TypeScript expertise. You understand the difference between a "working" UI and a "resilient" one., • You communicate clearly with both technical and non-technical stakeholders. You can explain why a pattern matters to someone who isn't a coder., • Ownership of a platform, not just a feature list, is what excites you., • Frontend: Vite, React 18, Wouter, Tailwind CSS, shadcn/ui, TanStack Query, Zod, • Middleware "Fast Path": Express.js, Node.js, Amazon Aurora (PostgreSQL), Websockets, • Infrastructure: AWS Amplify/Cognito, AppSyncEvents/GraphQL, OpenAPI, • Reliability: Storybook, Playwright/Cypress, Vitest, jest-dom, Visual Regression tools, GitHub workflows ~ We're rebuilding how insurance agencies operate using AI, automation, and modern infrastructure to amplify what actually drives the industry: relationships, trust, and operational excellence. Our tech replaces legacy workflows with a modern platform built on a full production backend: event-driven architecture, AI-powered automation, real-time data pipelines, and deep integrations across the insurance stack. The backend is serious. The frontend needs to match it. Equal Parts offers a comprehensive benefits package designed to support your health, well-being, and financial stability. • Major Medical Health Insurance with employer contributions toward premiums

About the Role We are seeking an exceptional, AI-native Senior+ Product Manager to build and lead the intelligence and reconciliation backbone of HTS across Flights, Hotels, Cars, and Ancillaries. This role owns the systems that define pricing intelligence, supplier performance reporting, and financial truth across millions of travel transactions. You will design and scale model-driven infrastructure that continuously interprets transactional data, generates reference pricing, detects performance shifts, reconciles financial flows, and governs revenue integrity in real time. This is a foundational role at the intersection of marketplace economics, machine learning, and financial systems. You will partner deeply with Data Science and Engineering to productionize predictive models, define evaluation frameworks, manage model lifecycles, and ensure intelligence systems are measurable, adaptive, and trusted. The work you do will directly shape partner economics, supplier transparency, and HTS’s global commerce platform. What would your day-to-day look like: * Own the end-to-end strategy and roadmap for pricing intelligence, supplier performance intelligence, and financial reconciliation across all travel verticals. Architect and evolve reference pricing systems leveraging predictive modeling, elasticity signals, competitive benchmarking, and real-time anomaly detection. * Build the supplier intelligence layer, transforming transactional data into automated performance signals, demand insights, and pricing diagnostics. * Establish and govern the canonical source of truth for revenue, margin, take rate, transactions, and cancellations across systems and partners. Design model evaluation, monitoring, and feedback frameworks, including drift detection and continuous improvement mechanisms. * Implement automated reconciliation and anomaly detection systems to proactively surface financial inconsistencies at scale. * Drive cross-functional execution across Engineering, Data Science, Finance, Commercial, and Supply to deliver production-grade, model-integrated infrastructure. An ideal candidate has: * 5+ years of Product Management experience, including ownership of AI-native products and model-driven systems in production. You have built and scaled systems where AI is foundational to product behavior — not an additive feature. * Hands-on experience writing prompts, designing evaluation harnesses, or prototyping with LLMs/foundation models as part of their PM workflow (not just overseeing others doing it) * Demonstrated experience operating as an AI-native PM, with hands-on ownership of model lifecycle management including problem framing, signal design, evaluation metrics (e.g., precision/recall tradeoffs, forecasting accuracy), monitoring frameworks, drift detection, and retraining strategies. * Experience shipping predictive systems, anomaly detection platforms, automated decisioning infrastructure, or model-driven pricing systems at scale. * Strong fluency with SQL and large-scale transactional datasets. * Experience owning pricing systems, financial reporting platforms, marketplace intelligence tools, or reconciliation frameworks. * Deep understanding of marketplace economics, revenue mechanics, and pricing strategy. * Track record of delivering complex, cross-functional systems with measurable business impact. * Exceptional communicator able to operate effectively across technical, financial, and commercial stakeholders. Bonus: * Prior experience in Data Science, Engineering, or a highly technical role. * Background in travel commerce, fintech, ads, or other transaction-heavy marketplace environments. Perks and benefits of working with us: * Well-funded and proven startup with large ambitions, competitive salary and the upsides of pre-IPO equity packages. * Unlimited PTO. * Carrot Cash travel stipend. * Access to co-working space on demand through FlexDesk AND Work-from-home stipend. * Please ask us about our very generous parental leave, much above industry standards!. * Entrepreneurial culture where pushing limits and taking risks is everyday business. * Open communication with management and company leadership. * Small, dynamic teams = massive impact. * 100% employer paid Medical, Dental and Vision coverage for employees. * Access to Disability & Life insurance. * Health Reimbursement Account (HRA). * DCA/ FSA and access to 401k plan. More about Hopper At Hopper, we are on a mission to become the leading travel platform globally – powering Hopper’s mobile app, website and our B2B business, HTS (Hopper Technology Solutions). By leveraging massive amounts of data and advanced machine learning algorithms, Hopper combines its world-class travel agency offering with proprietary fintech products to bring transparency, flexibility and savings to travelers globally. We have developed several unique fintech solutions that address everything from pricing volatility to trip disruptions – helping people travel better and save more on their trips. The Hopper platform serves hundreds of millions of travelers globally and continues to capture market share around the world. he Hopper app has been downloaded over 120 million times and has become largely popular among younger travelers – with 70% of its users being Gen Z and millennials. While everyone knows us as the Gen Z and Millennial travel app, Hopper has evolved to become much more than that. In recent years, we’ve grown into a travel fintech provider, commerce platform, and global travel agency that powers some of the world’s largest brands. Through HTS, our B2B division, the company supercharges its partners’ direct channels by integrating our fintech products on their sites or powering end-to-end travel portals. Today, our partners include leading travel brands like Capital One, Nubank, Air Canada, and many more. Here are just a few stats that demonstrate the company’s recent growth: Billions of dollars worth of travel and travel fintech are sold through Hopper and HTS’ channels every year. Our fintech products – including Cancel for Any Reason and Flight Disruption Assistance – have exceptionally strong CSAT because the terms are always clear, and customers receive instant, no-questions-asked resolutions. Almost 30% of our app customers purchase at least one fintech product when making a booking; and consumers are 1.6x more likely to repurchase if they add fintech to their booking vs if they booked just travel. Given the success of its fintech products, Hopper launched a B2B initiative, HTS (Hopper Technology Solutions), which represents more than 75% of the business. Through HTS, any travel provider (airlines, hotels, banks, travel agencies, etc.) can integrate and seamlessly distribute Hopper’s fintech or travel inventory on their direct channels. As its first HTS partnership, the company partnered with Capital One to co-develop Capital One Travel, a new travel portal designed specifically for cardholders. Other HTS partners include Air Canada, Uber, CommBank, Nubank, Flair Airlines and many more. Hopper has been named the #1 most innovative company in travel by Fast Company Hopper has been downloaded over 120 million times and continues to have millions of new installs each month. Hopper is now the #3 largest online travel agencies in North America and 70% of our app customers are Gen-Z and millennials travelers. Hopper has raised over $750 million USD of private capital and is backed by some of the largest institutional investors and banks in the world. HTS is primed to continue its growth as the leading travel ecommerce provider in a $1 trillion online shopping category. The Hopper app and website will also continue to be the preferred travel provider for Gen Z and Millennials.. Come take off with us! #LI-REMOTE

Octane® is revolutionizing recreational purchases by delivering a seamless, end-to-end digital buying experience. We connect people with their passions by combining cutting-edge technology and innovative risk strategies to make lifestyle purchases - like powersports vehicles, RVs, and OPE - fast, easy, and accessible. Octane adds value throughout the customer journey: inspiring enthusiasts with our editorial brands, including Cycle World® and UTV Driver®, instantly prequalifying consumers for financing online, routing customers to dealerships for an easy closing, and supporting customers throughout their loan with superior loan servicing. Founded in 2014, we’re a company with 550+ employees and over 30 OEM and 4,000 dealer partners. At Octane, we’re building innovative technology solutions that power smarter, faster, and more efficient decision-making. We are seeking an accomplished AI/ML Technical Lead to drive the design, development, and deployment of advanced machine learning solutions that scale seamlessly in production environments. In this role, you’ll shape the strategy for how AI is leveraged across the organization, partnering with cross-functional teams to translate business goals into technical solutions that are not only state-of-the-art, but also ethical, secure, and reliable. This position is ideal for a hands-on leader who thrives at the intersection of research and engineering. You will mentor engineers and data scientists, establish best practices for MLOps, and ensure models deliver consistent value long after deployment. With opportunities to explore cutting-edge fields like generative AI, large language models, natural language processing, and computer vision, you’ll play a pivotal role in setting the technical standard for Octane’s AI initiatives while guiding the team toward innovation and measurable impact. Responsibilities: • Lead the end-to-end design, development, and deployment of advanced AI/ML systems, ensuring production environments are scalable, efficient, and highly performant., • Collaborate with cross-functional teams to define AI strategy, evaluate use cases, and translate business objectives into actionable technical solutions., • Build, optimize, and maintain machine learning pipelines, covering data ingestion, preprocessing, model training, evaluation, deployment, and ongoing monitoring., • Establish and champion MLOps best practices, including CI/CD for ML workflows, automated testing frameworks, and continuous model performance monitoring to prevent drift., • Serve as a technical mentor and leader, guiding engineers and data scientists on system architecture, coding standards, and efficient development practices., • Research and evaluate emerging AI methodologies, frameworks, and tools, integrating innovative approaches into the organization’s technology stack where applicable., • Ensure AI solutions meet ethical, security, and compliance standards, including model explainability, bias mitigation, and data privacy requirements., • Partner with engineering leadership to set technical strategy, establish architectural guidelines, and define standards that shape the future of AI initiatives at Octane. Requirements: • Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field with 5+ years of professional experience, or Bachelor’s degree with 8+ years of experience in AI/ML engineering., • Proven expertise with leading ML/DL frameworks such as TensorFlow, PyTorch, or JAX, with hands-on experience training and deploying models., • Strong programming proficiency in Python, with additional experience using languages such as C++ or Java for performance-critical applications., • Practical experience with MLOps tools and workflows, including MLflow, Kubeflow, SageMaker, or Vertex AI to streamline model lifecycle management., • Deep understanding of data engineering concepts such as data pipelines, preprocessing, and distributed computing, as well as hands-on experience with cloud platforms (AWS, Azure, or GCP)., • Demonstrated success designing, deploying, and optimizing AI/ML systems at scale, including model serving, monitoring, and iteration., • Strong communication and collaboration skills, with the ability to influence technical direction and align diverse stakeholders. Preferred Skills: • Experience with generative AI and large language models (LLMs), including prompt engineering and fine-tuning approaches., • Knowledge of advanced ML domains such as natural language processing (NLP), computer vision, reinforcement learning, or recommendation systems., • Familiarity with containerization and orchestration technologies, including Docker and Kubernetes, to support scalable deployment., • Hands-on experience with infrastructure-as-code (IaC) tools, such as Terraform or Ansible, to enable automated and reproducible infrastructure management., • Contributions to the AI/ML community, such as open-source projects, conference presentations, or peer-reviewed research publications. Compensation: In addition to salary, Total Rewards include bonus eligibility, a stock option package, and benefits as outlined below. The role described above offers a base salary of $150,000 to $210,000 + bonus incentive eligibility. Your offer will be based on location, the alignment of your qualifications with the requirements of the job and internal equity. Benefits: • Robust Health Care Plans (Medical, Dental & Vision), • Generous Parental Leave, • Flexible Time Off (FTO) Policy - Time Off When You Need It, • Retirement Plan (401k) with company match!, • Educational Assistance/Tuition Reimbursement up to $3K/year, • Life Insurance (Basic, Voluntary & AD&D), • Short Term / Long Term Disability, • Robust Ancillary benefits including accident insurance, hospital insurance, etc, • Wellhub (Gympass) Wellness Benefit, • Powersports Safety Benefit Disclaimer: The above statements are intended to describe the general nature and level of work being performed by associates assigned to this classification. They are not to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified. All personnel may be required to perform duties outside of their normal responsibilities from time to time, as needed. Octane Lending is an equal opportunity employer committed to providing equal employment opportunity without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other protected status with respect to recruitment, hiring, promotion and other terms and conditions of employment. #LI-MZ1 #LI-Hybrid

SurveyMonkey is the world’s most popular platform for surveys and forms, built for business—loved by users. We combine powerful capabilities with intuitive design, effectively serving every use case, from customer experience to employee engagement, market research to payment and registration forms. With built-in research expertise and AI-powered technology, it’s like having a team of expert researchers at your fingertips. Trusted by millions—from startups to Fortune 500 companies—SurveyMonkey helps teams gather insights and information that inspire better decisions, create experiences people love, and drive business growth. Discover how at ___. What we’re looking for We're looking for a Principal PM to own one of our most strategic product bets. This is a role for someone who thinks in systems, operates at the intersection of product and platform, and knows how to turn a long-term vision into a quarterly plan that actually ships. You'll work at the senior individual contributor level with a player-coach remit, setting strategy, influencing cross-functional teams, and developing the PMs around you. This is not a role for someone who wants to manage up and delegate down. It's for someone who still loves the work What you’ll be working on • Own strategy and roadmap for a high-priority initiative with direct revenue impact, • Define the long-term vision while staying close enough to execution to catch when things drift, • Influence and align cross-functional teams such as engineering, design, data, and adjacent product orgs, • Mentor and elevate the PMs around you through example, not just feedback, • Bring commercial rigor to every roadmap decision connecting features to outcomes, • Work with platform and infrastructure teams to define what capabilities are needed and in what order We’d love to hear from people with • 8+ years in product management with a track record of owning complex, cross-functional bets, • Deep experience in one or more of: workflow automation, orchestration, engagement, or growth product loops, • Strong strategic long term and short term instincts, • Commercial orientation thinking about revenue impact, retention, and activation as naturally as you think about user experience, • Proven ability to influence without authority across large, matrixed organizations, • Providing player-coach energy to be in the room where the hard decisions get made, not just reviewing the output, • Comfort with AI and data as product primitives — you know how to build things that feel intelligent, • Experience at a SaaS company serving SMB or mid-market customers, • Background in integration-heavy products or platforms with rich API ecosystems, • Familiarity with AI-powered recommendation or next best action systems The base pay provided for this position ranges from $208,250.00/year - $208,250.00/year depending on the geographic market and assuming a full-time schedule. Actual base pay is based on a number of factors including market location, job-related knowledge, education or training, skills, and experience. Bonuses and commissions may also be offered as part of the total compensation package, in addition to a competitive benefits package including medical, dental, vision, life, and disability insurance; 401(k) retirement plan; flexible spending & health savings account; paid holidays; paid time off; employee assistance program; and other company benefits. SurveyMonkey believes in-person collaboration is valuable for building relationships, fostering community, and enhancing our speed and execution. While this role is remote, it may require in-person participation. You will be encouraged to attend company events throughout the year. These events will take place at a designated SurveyMonkey office or location. #LI-remote Why SurveyMonkey? We’re glad you asked At SurveyMonkey, curiosity powers everything we do. We’re a global company where people from all backgrounds can make an impact, build meaningful connections, and grow their careers. Our teams work in a flexible, hybrid environment with thoughtfully designed offices and programs like the ___ to help employees thrive in work and life. We’ve been trusted by organizations for over 25 years, and we’re just getting started. Our milestones include celebrating a quarter-century of curiosity with___, opening new hubs in ___ and ___, crossing the threshold of ___, and earning recognition as one of the Most Inspiring Workplaces across ___ and ___. We live our___—like championing inclusion and making it happen—by embedding them into how we hire, collaborate, and grow. They help shape everything from our culture to our business decisions. Come join us and see where your curiosity can take you. Our commitment to an inclusive workplace SurveyMonkey is an equal opportunity employer committed to providing a workplace free from harassment and discrimination. We celebrate the unique differences of our employees because that is what drives curiosity, innovation, and the success of our business. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate. Accommodations are available for applicants with disabilities. Your data For more information on how SurveyMonkey (including its subsidiary and affiliated companies) processes your personal data as a job candidate or applicant, please see our ___. Please note that we may use artificial intelligence (AI) tools to support parts of the hiring process, such as sourcing candidates, reviewing applications, analyzing resumes, or summarizing interviews. These tools assist our recruitment team but do not replace human judgment.

Job Description Upbound is redefining how modern infrastructure is built for the Agentic AI Era. We're the creators and primary maintainers of Crossplane, and we're building the Intelligent Control Plane—a new platform layer that makes infrastructure programmable, autonomous, and composable. Our mission is to power the AI-native enterprise with a foundational platform layer that helps teams provision, operate, and adapt infrastructure at scale—so platforms are ready for both humans and AI agents. We partner with leading cloud providers, ISVs, and open-source communities to help organizations move faster with greater confidence. Today, Upbound supports Fortune 500 companies and platform engineers across 100+ countries. Crossplane has surpassed 100M+ downloads and is used by 1,000+ teams worldwide. We're a Series B company backed by GV (formerly Google Ventures), Altimeter Capital, and Intel Capital, and we've raised $69M to date. Learn more at upbound.io. As a Senior Customer Support Engineer at Upbound, you won't just resolve tickets, you'll own the customer experience from first touch to full resolution, setting the standard for what world-class technical support looks like in the cloud-native ecosystem. You'll be a trusted technical advisor to our customers, working alongside Product, Engineering, and Customer Success to make every interaction exceptional. This role reports into the Customer Experience team at Upbound a signal of how seriously we take support as a strategic function, not an afterthought. This is a high-impact role for someone who gets genuine satisfaction from solving hard problems, has a natural drive to help, and believes that great support is a craft not a function.In this role, you will: • Serve as a primary technical resource for customer inquiries related to Upbound's products Spaces, Cloud, Official Extensions, Marketplace, Registry & More delivering a "wow" experience with every single touch, • Apply a structured, methodical troubleshooting approach to diagnose and resolve complex customer issues, moving efficiently from hypothesis to root cause, • Triage, own, and drive tickets to resolution with urgency and clear communication proactively keeping customers informed every step of the way, • Identify, document, and escalate bugs and product issues to Engineering with thorough reproduction steps, collaborating closely to ensure fast resolution, • Partner with the Solutions team on complex, strategic customer requests, ensuring seamless handoffs and continuity of experience, • Capture and contribute knowledge back to the team building and maintaining a high-quality internal knowledge base using KCS (Knowledge-Centered Service) methodology, • Gather and synthesize customer feedback to surface trends, advocate for product improvements, and drive process enhancements across the team, • Build or contribute to agentic tooling, automation, and internal workflows that meaningfully elevate the support experience and scale your own impactYou're a good fit if you have:, • 4+ years of experience in a technical support or customer-facing engineering role, ideally in a SaaS or enterprise infrastructure environment, • A natural "helper gene" you're energized by solving problems for others and take personal ownership of customer outcomes, • Strong, systematic troubleshooting methodology you approach complex issues with a clear framework, not just instinct, • Solid experience with Kubernetes, cloud-native technologies, and infrastructure-as-code, • Excellent written and verbal communication skills you can explain deeply technical concepts to any audience without losing precision, • A customer-first mindset with a relentless drive to deliver experiences that genuinely delight, • Solid experience with Kubernetes and cloud-native technologies — including Helm, OCI artifact distribution, RBAC, admission webhooks, and policy frameworks (OPA/Gatekeeper, Kyverno), • Able to drive root cause analysis independently using observability tooling — Prometheus, Grafana, Loki, or equivalent — without needing Engineering to interpret metrics or traces for you, • Hands-on experience with Upbound and/or Crossplane, • Familiarity with KCS (Knowledge-Centered Service) practices and methodology, • Experience building agentic tooling, support automations, or AI-assisted workflows to improve customer outcomes, • Proficiency with Unix-like shells and command-line interfaces, • Scripting skills in Python, Bash, or similar, • Startup experience you know how to operate with ambiguity and move fast, • Hands-on experience with GitOps tooling (ArgoCD or Flux) and familiarity with reconciliation failure modes — drift, conflicts, dependency ordering, • Background in platform engineering or internal developer platforms (IDPs) — you understand what platform teams are trying to build and why it breaks, • Working proficiency in Go — enough to read Crossplane/provider source and identify relevant fixes #LI-REMOTE Why Upbound? At Upbound, you'll help shape the systems and strategies that drive predictable, scalable growth in a product-led company embracing usage-based models. If you're excited to build from the ground up, work with cutting-edge cloud technologies, and directly impact how revenue is generated and scaled—this is your seat at the table. About Upbound Upbound is pioneering infrastructure platforms for the Agentic AI Era, serving Fortune 500 companies and platform engineers across more than 100 countries. The company empowers infrastructure and platform teams with Intelligent Control Planes - based on Kubernetes and Crossplane - that provision, operate, and adapt so platforms are ready for both humans and AI agents. Upbound is the creator and primary maintainer of Crossplane, the popular open-source framework for building cloud-native control planes, with over 100 million downloads and adoption by more than 1,000 teams worldwide. A Series B startup backed by GV (formerly Google Ventures), Altimeter Capital, and Intel Capital, Upbound has raised $69M to date. For more information, visit .

Business Area: Professional Services Seniority Level: Mid-Senior level Job Description: At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world's largest enterprises. As an AI Solutions Engineer within Cloudera's Public Sector Consulting team, you will be the technical architect and execution lead for agencies moving from "data chaos" to "agentic autonomy." You will work directly with government organizations to design, build, and deploy mission-critical AI applications on the Cloudera Data Platform (CDP). This is not a "theoretical" role. You will be on the front lines of Phase 2 and Phase 3 adoption journeys-helping customers clean legacy data silos, select the right model architectures, and industrialize MLOps pipelines in highly secure, often air-gapped or hybrid-cloud environments. As the AI Solutions Engineer you will: 1. AI Model Strategy, Selection and Implementation * Evaluate and select optimal model architectures (LLMs, SLMs, or traditional ML) based on mission requirements, considering tradeoffs between accuracy, latency, and cost. * Guide customers on "Build vs. Buy vs. Fine-tune" decisions, prioritizing open-source models (Llama, Mistral, Falcon) that can run securely within a sovereign data perimeter. * Experience building Agentic Workflows (AI agents that can execute API calls and multi-step tasks). 2. End-to-End Data Engineering * Design and implement robust data pipelines within CDP to transform "messy" legacy data into AI-ready formats. * Develop and optimize Vector Databases and Retrieval-Augmented Generation (RAG) architectures to ground AI responses in verified agency facts. * Build Data pipelines with Spark, Nifi, Kafka or other ETL tools. 3. Optimization & Performance Tuning * Optimize model inference for production environments using quantization, pruning, and hardware acceleration (NVIDIA GPU orchestration). * Implement LLMOps to monitor model performance, detect hallucination rates, and manage model versioning and drift. 4. Public Sector Advisory & Governance * Collaborate with the customer's AI Center of Excellence (CoE) to establish automated guardrails for ethics, bias mitigation, and FedRAMP/IL5 compliance. * Translate complex technical AI concepts into mission-value briefings for GS-level stakeholders and agency leadership. We're excited about you if you have: (Minimum Qualifications): * Experience: 5+ years in Data Engineering, Machine Learning, or Software Engineering, with at least 2 years focused on Generative AI or Deep Learning. * Technical Stack: Expertise in Python and deep learning frameworks (PyTorch, TensorFlow, Hugging Face). * Hands-on experience with Cloudera (CDP), Spark, or similar big data ecosystems. * Proficiency in orchestration tools like LangChain, LlamaIndex, or Haystack. * Experience developing visual data representations and dashboards (Django, React, or Angular) * Experience using a compiled programming language, preferably one that runs on the JVM (Java, Scala, etc) * Data Expertise: Proven ability to build ETL/ELT pipelines and work with both SQL and NoSQL/Vector databases (e.g., Pinecone, Milvus, or PGVector). * Public Sector Knowledge: Understanding of government security frameworks (NIST AI RMF, FedRAMP, SRGs, STIGs). * Active Top Secret Security Clearance You may also have: (Preferred Qualifications) * Experience fine-tuning of foundational models using techniques such as PEFT (Parameter-Efficient Fine-Tuning) and LoRA to adapt AI to domain-specific government nomenclature. * Experience training of specialized models on proprietary datasets while ensuring strict adherence to data privacy and sensitivity labels. * Experience installing and operating Cloudera Data Platform * Experience installing and operating Kubernetes * Experience in Air-Gapped deployments and managing AI workloads in disconnected environments. * Advanced degree (MS or PhD) in Computer Science, Data Science, or a related field. * Active Counterintelligence (CI) or Full Scope (FS) Poly is highly preferred. This role is not eligible for immigration sponsorship. What you can expect from us: * Generous PTO Policy * Support work life balance with ___ * Flexible WFH Policy * Mental & Physical Wellness programs * Phone and Internet Reimbursement program * Access to Continued Career Development * Comprehensive Benefits and Competitive Packages * * Employee Resource Groups EEO/VEVRAA #LI-MH2 #LI-Remote

Datadog, Prometheus, SumoLogic) OpenSearch Linux Responsibilities Construct infrastructure as code and enforce best practice across configurations while preventing drift between Terraform configurations and infrastructure deployments Design infrastructure that enables Engineering, Data Science,

Job DescriptionDescription: Better Ingredients. Better Pizza. Better Leaders. At Papa John's, we believe the quality of our product starts with the quality of our people, and our Shift Leaders are proof of that. You're the one who keeps the standard high when the Store Team Leader steps out, who rallies the crew when the dinner rush hits, and who makes sure every customer interaction ends the right way. You don't just manage a shift. You own it. The Role You'll work side by side with the Store Team Leader to keep daily operations running at the level our guests expect. In their absence, the restaurant is yours to lead. That means directing your crew with confidence, making real-time decisions under pressure, holding the quality bar without compromise, and bringing the kind of energy that makes your team want to match it. This role is for someone who's already shown they can be counted on, and is ready to take on more responsibility. What You'll Be Responsible For Your Team. You lead from the front. You'll coach crew members in the moment, jump in where needed, and create a shift culture that's focused, fun, and built on accountability. Your team will perform better because you're there. Your Guests. Papa John's has built its reputation on quality and care, and you protect both every shift. You'll handle customer concerns with composure and professionalism, turning difficult situations into lasting positive impressions. Your Operations. From opening duties to closing checklists, order accuracy to food safety standards, you'll keep every workstation running at Papa John's standard. You know what right looks like, and you don't let the shift drift from it. Your Numbers. You understand that how you run a shift directly impacts the business. You'll help monitor performance, flag inefficiencies, and support the Store Team Leader in building the habits and procedures that drive profitability. Requirements: You're calm under pressure, clear in your communication, and consistent in your follow-through. You coach without micromanaging, solve problems without escalating drama, and hold yourself to the same standard you hold your crew. You're curious, coachable, and hungry to keep growing. Prior leadership or shift lead experience is preferred. Must be 18 or older. Food Handler Certification required or must be obtained before first shift. What We Offer • Competitive hourly pay, • Medical, dental, and vision insurance, • 401(k), • Paid time off, • Paid training and ongoing development, • Flexible scheduling, • Employee discount on Better Ingredients, Better Pizza Shift Leader is where we develop our next Operating Managers. The experience you build here, running operations, developing crew members, managing in-the-moment decisions, is exactly the foundation our best store leaders are built on. We promote from within and we'll show you exactly what your growth path looks like from day one. Ready to lead? Apply today.

Job Description Who We Are Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our 600+ employees work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do to deliver unique ad experiences across the world's most premium platforms. Taking a creative science approach to all we do, we continuously innovate solutions that outperform industry benchmarks and client expectations. Now 20+ years strong, Kargo has offices in NYC, Chicago, LA, Dallas, Sydney, Auckland, London and Waterford, Ireland. Who We Hire Techies who want to build the future. Creatives who want to design it better. Communicators to win business. Collaborators to build it. Data pros who turn numbers into insights. Product builders who turn ideas into innovations. Anyone eager to be on a team that doesn't stop to ask what's next, because they're already building it. Mission The Project Manager, Backend & SRE exists to bring predictability, clarity, and momentum to Kargo's most technically complex delivery work. This role owns the coordination layer between backend engineers, SREs, and cross-functional stakeholders — turning ambiguous technical initiatives into well-scoped, well-tracked, and reliably shipped outcomes. When this role is firing, engineering teams spend less time on coordination overhead and more time building, and stakeholders always know where things stand. Outcomes — What Success Looks Like in 6–12 Months • Backend and SRE initiatives are delivered on time and with visibility — Assigned projects have clear plans, tracked milestones, and stakeholder-facing status updates that don't require chasing engineers for answers, • Reliability and operational work is planned alongside feature work — On-call follow-ups, incident remediation, and technical debt are visible in sprint planning and not perpetually deprioritized in favor of new features, • Cross-team dependencies are surfaced early, not discovered late — Risks and blockers across services, systems, and teams are identified proactively and either resolved or escalated before they become delivery failures, • Incident coordination is faster and post-incident learnings stick — Communication during incidents is organized and timely; action items are tracked to completion and retrospective findings are incorporated into future planning Required • 3–5 years of project management experience embedded with software engineering teams — not adjacent to them, but working directly in their delivery cadence, • Experience supporting backend, platform, infrastructure, or SRE teams; comfortable with technical terminology, system dependencies, and operationally critical workflows without needing to implement them, • Strong command of Agile delivery — sprint planning, backlog refinement, retrospectives, and velocity tracking practiced consistently, not ceremonially, • Proficiency with Jira or equivalent tools for maintaining accurate project plans, timelines, and delivery artifacts, • Ability to translate complex technical work into clear, concise status updates for product and non-technical stakeholders — without losing the important details, • Familiarity with incident management, on-call workflows, or reliability-focused processes in a high-availability environment Proactive Risk Management • Identifies delivery risks, capacity constraints, and cross-team dependencies before they become problems — doesn't wait for engineers to surface issues, • Earns trust with engineers by demonstrating genuine understanding of backend and SRE work — asks smart questions, respects technical constraints, and never oversimplifies, • Takes full accountability for the delivery artifacts, timelines, and coordination work in their scope — doesn't wait to be asked and doesn't let things drift, • Keeps distributed, cross-functional teams aligned through consistent, concise updates — no one is surprised by a missed milestone or a hidden risk, • AdAge Best Places to Work, • ThinkLA Partner of the Year, • Built In Best Places to Work, • Cynopsis 2025 Top Women in Media - Jeannine Shao Collins, • Martech Breakthrough Awards - Best Overall Adtech Company, • Digiday Media Awards Best Event, • Cynopsis Media Impact Awards-Best CTV Platform, • Martech Breakthrough Awards-CTV Innovation, • Big Picture: kargo.com Pursuant to applicable fair chance laws, including the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Kargo will consider qualified applicants with arrest and conviction records for employment.

Job DescriptionSamba is a media intelligence company. We know what the world is watching, reading, and thinking about — in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it’s the most interesting data asset on the planet, because it’s the most culturally relevant. We're looking for a Conversational Marketing Specialist to join our Growth Marketing team and own the 1:1 channels that turn high-intent accounts into qualified pipeline. This role sits at the intersection of marketing and sales development and is built for someone hungry to build the future of this discipline with AI. You'll design, launch, and optimize conversational campaigns across web chat, email, and SMS while partnering directly with sellers to move deals forward and ensure their book of business is covered in your campaigns. You're equal parts campaign builder, copywriter, and automation architect — and you're already using AI to build automations and interesting marketing campaigns.WHAT YOU'LL DO • Design and manage multi-channel conversational campaigns — web chat (live + automated flows), 1:1 email, SMS, and targeted phone outreach — that generate and accelerate pipeline from qualified lead through qualified opportunity., • Own the web chat channel end to end: build automated chat flows for the broader audience and manage a live chat experience visible only to high-value B2B visitors, routing conversations to sellers at the right moment., • Send targeted 1:1 emails and outreach on behalf of sellers from Gong Engage, following up with leads and proactively mining the database for accounts to nurture, re-engage, and push to conversation., • Partner with marketing ops to turn your best-performing campaigns into scalable automations — you design the workflow, write the copy, define the triggers and branching logic, and hand off a campaign-ready spec., • Build and refine persona-specific messaging across Samba TV's key buyer segments — you'll learn multiple personas deeply and adapt tone, positioning, and value props for each., • Manage and optimize HubSpot and Salesforce workflows for lead tracking, nurture sequencing, and performance measurement — maintaining clean data and tight process as you scale., • Comb the funnel for gaps: identify where leads stall, what data we are missing, or where messaging falls flat, where a new channel or tool could unlock conversion — then you'll build the fix., • Track your numbers obsessively. Own MQL → SQL → opportunity → pipeline → revenue metrics and report on campaign performance weekly, iterating based on what the performance data tells you to do., • Have 2–5 years of experience in B2B demand gen, marketing campaigns, BDR/SDR, or conversational marketing — you understand the full funnel and can operate across it., • Are fluent in HubSpot and Salesforce, comfortable in tools like Gong Engage, 6sense, Drift/Qualified, and modern martech — and can learn new platforms fast., • Have hands-on experience designing campaigns with automation logic: triggers, branching, enrollment criteria, suppression lists — you think in workflows, not just copy., • Write sharp, on-brand 1:1 communications — emails, chat scripts, call talk tracks — that sound human, land with the right persona, and drive action., • Understand ABM principles and can execute account-based plays that coordinate across channels and align with sales priorities., • Have demonstrated experience interpreting marketing and sales data to steer outreach strategy, optimize campaigns, and report results to stakeholders., • Are AI-native. You use Claude, Cowork, or comparable AI tools daily across your workflows — outreach drafting, lead research, campaign design, reporting, personalization. This is a requirement, not a nice-to-have., • Can navigate complex B2B sales cycles, technical products, and large organizations — driving work forward with imperfect information., • Are a cross-functional operator who builds strong working relationships with sales, marketing ops, design, and product teams., • Have a high bar for quality but know how to balance polish with velocity to hit your numbers., • Strong MQL → SQL → opportunity conversion rates driven by your conversational campaigns., • Measurable pipeline contribution tied directly to your channels — web chat, 1:1 email, SMS, and seller-assisted outreach., • A library of automated campaign workflows handed off to ops and running with quality at scale., • Deep persona fluency that shows up in messaging quality and conversion rates.

Drift In is seeking a Floor Manager with elevated, high-volume restaurant experience. Drift In is a vibrant outdoor restaurant in the West Village overlooking the Hudson River. With two distinct waterfront seating areas, Drift In features easy-going beach fare, craft cocktails, summery wines, crisp