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Skool is hiring its first ever Data Scientist. You’ll join our Data team, report to our Head of Data, and help shape how data science is used across the company. This is a hands-on role for someone who wants to grow a consumer product by building new ways for users to discover the right communities and people, including feed, search, recommendations, and matchmaking, and by testing what drives engagement and growth. One of our biggest areas of focus is helping users find the right communities and people on Skool. You’ll help us build and improve that experience through recommendations, ranking, and personalization, while also partnering closely with Product, Growth, and Engineering on engagement, activation, retention, and advertising. We’re looking for someone who can dig into ambiguous problems, find the real opportunity, and turn analysis into action. This is a 100% remote position. Skool has an office in Los Angeles, but we work as a distributed team. What you’ll do • Build models and algorithms for feed, recommendation, ranking, and search to help users discover relevant communities and people, • Own data science work across growth, product, and user engagement, from analysis through measured results, • Use real-time data to find opportunities, test ideas quickly, and refine models based on outcomes, • Develop predictive models for activation, retention, and other key funnels, • Design and run experiments, analyze results, and turn findings into clear product and business decisions, • Partner with Growth to test and improve advertising, including measuring its impact on acquisition and engagement What we’re looking for • You have deep data science experience and a track record of using analysis, experiments, and models to grow a consumer product, • You have hands-on experience building recommendation, matchmaking, ranking, search, or personalization systems, • You have a strong foundation in machine learning and statistical modeling using tools such as Python and scikit-learn, • You favor quick action and experimentation over long analysis and can build enough conviction in your work to bet on it, • You are comfortable managing real budget, being wrong, learning quickly, and making the next bet, • You can own ambiguous problems, decide what matters, and move without waiting for permission, • You are strong at data analysis and tool-agnostic. You care about accurate results and business impact more than a specific stack, • You can write enough production-quality code to implement or partner closely on real-time solutions when offline analysis isn’t enough, • Bonus: You have hands-on experience optimizing advertising, acquisition, or other performance marketing systems Why join Skool? • Over 30M users, 150K communities, $1B in creator revenue + already profitable (!), • Fast-growing, mission-driven company with a focus on education and community, • We’re a small, collaborative team that likes to have fun while prioritizing balance, • Remote-first, industry-leading compensation, fully covered medical, dental, and vision insurance (including dependents), 401(k) matching, and more!

Applied Scientist (Remote | $245–$280/hr) Position: Applied Scientist Type: Contractor Compensation: $245–$280 per hour Location: Remote About the Opportunity An AI-focused project is seeking Applied Scientists to contribute expertise in advanced analytics, experimentation, statistics, and machine learning. Contractors will help improve next-generation AI systems by evaluating model outputs, developing high-quality datasets, and providing expert feedback. Prior AI experience is not required. Key Responsibilities • Design and execute statistical analyses to uncover insights from complex datasets., • Develop, validate, and evaluate machine learning models for business and research objectives., • Apply data modeling techniques and manage data transformations using Python and Pandas., • Collaborate with stakeholders to establish experimentation frameworks and analyze outcomes., • Document methodologies, findings, and recommendations clearly and accurately., • Review peer contributions and provide constructive technical feedback., • Deliver reproducible code and organized analytical reports., • Evaluate AI model outputs involving statistics, machine learning, experimentation, and quantitative reasoning., • Create expert-level prompts, datasets, and reference solutions., • Identify flawed methodologies, statistical errors, and weaknesses in quantitative reasoning., • Provide structured feedback to improve AI model performance. Required Qualifications • Strong proficiency in Python and data analysis., • Knowledge of machine learning, statistics, experimentation, and data modeling., • Experience with Pandas and related data science tools., • Strong analytical, problem-solving, and quantitative reasoning skills., • Excellent written and verbal communication skills. Preferred Qualifications • At least one year of experience at a leading company in technology, finance, or research., • Proficiency with Python libraries such as Pandas and scikit-learn., • Demonstrated experience in applied statistics, experimental design, and machine learning., • Experience developing, tuning, and evaluating machine learning models., • Ability to work with structured and unstructured data., • Experience collaborating on cross-functional or open data science projects., • Advanced academic background or practical experience in data science, applied mathematics, or a related field. Compensation • Hourly compensation of $245–$280., • Remote, contractor-based engagement. Application Process The application process was not specified in the provided listing.

Applied Scientist (Remote | $245–$280/hr) Position: Applied Scientist Type: Contractor Compensation: $245–$280 per hour Location: Remote About the Opportunity An AI-focused project is seeking Applied Scientists to contribute expertise in advanced analytics, experimentation, statistics, and machine learning. Contractors will help improve next-generation AI systems by evaluating model outputs, developing high-quality datasets, and providing expert feedback. Prior AI experience is not required. Key Responsibilities • Design and execute statistical analyses to uncover insights from complex datasets., • Develop, validate, and evaluate machine learning models for business and research objectives., • Apply data modeling techniques and manage data transformations using Python and Pandas., • Collaborate with stakeholders to establish experimentation frameworks and analyze outcomes., • Document methodologies, findings, and recommendations clearly and accurately., • Review peer contributions and provide constructive technical feedback., • Deliver reproducible code and organized analytical reports., • Evaluate AI model outputs involving statistics, machine learning, experimentation, and quantitative reasoning., • Create expert-level prompts, datasets, and reference solutions., • Identify flawed methodologies, statistical errors, and weaknesses in quantitative reasoning., • Provide structured feedback to improve AI model performance. Required Qualifications • Strong proficiency in Python and data analysis., • Knowledge of machine learning, statistics, experimentation, and data modeling., • Experience with Pandas and related data science tools., • Strong analytical, problem-solving, and quantitative reasoning skills., • Excellent written and verbal communication skills. Preferred Qualifications • At least one year of experience at a leading company in technology, finance, or research., • Proficiency with Python libraries such as Pandas and scikit-learn., • Demonstrated experience in applied statistics, experimental design, and machine learning., • Experience developing, tuning, and evaluating machine learning models., • Ability to work with structured and unstructured data., • Experience collaborating on cross-functional or open data science projects., • Advanced academic background or practical experience in data science, applied mathematics, or a related field. Compensation • Hourly compensation of $245–$280., • Remote, contractor-based engagement. Application Process The application process was not specified in the provided listing.

Leadership in data science: experience leading cross-functional teams of data scientists, data engineers, and data or cost analysts, and defining a data strategy aligned to organizational goals. Knowledge and experience performing cost estimating and analysis on major defense systems, including typi

People Analytics & Research is a strong team of Data Scientists, Data Engineers, and Analysts. We need a Data Scientist with a proven track record of translating data into insight and action. Experience applying data science to compensation, recruiting, or broader workforce and organizational questi

The projects that a Senior Environmental Scientist works on will include low to high complexity, multi-media, multi-contaminant remediations. The Senior Environmental Scientist is responsible for planning and implementing field activities, evaluating laboratory data and measurements collected during

You will collaborate closely with data analysts, account managers, product teams and engineering to understand our business and rapidly iterate on solutions that maximize ROI. As an AI Engineering team member, you will have the opportunity to leverage cutting-edge data science, machine learning and

Safety & Mods Data Science operates as a closed-loop system: Safety Insights helps the organization understand, measure, and prioritize risk, while Safety Enforcement Data Science builds and optimizes the models, rules, and systems that detect and mitigate abuse effectively, efficiently, and fairly.

As an AI Engineering team member, you will have the opportunity to leverage cutting-edge data science, machine learning and AI on petabytes of data in ways that make a large and lasting impact on the company. You will collaborate closely with data analysts, account managers, product teams and engine

Shopmonkey has the structured data and workflows to support serious data science work, and you'll have the rare opportunity to help build the analytical and ML foundation from the ground up, shaping how data-driven decision making scales alongside a maturing platform and defining what the future of

Company Description RP-IO helps organizations hire and scale more efficiently. We are actively recruiting for a Senior Data Scientist for a Contract, remote position, for one of our customers based in the Bay Area. Why This Role Exists Our client (Company) is integrating with another company (Parent Company - PC). The PC is taking over Company's prospect account scoring in stages and folding in its own marketing, outbound, pre-approval, and credit models. It is building the replacement models on their current data. Until that finishes, their production scoring system still decides which accounts outbound reps work. Over the engagement, their systems will increasingly run on PC's scores instead. This role runs the transition end to end. It hands PC the Company's training data, validates PC's scores against Company's, integrates and applies those scores in Company's sales targeting, and keeps the current system healthy until cutover. It is one project with a defined end. It also frees the data scientist who built the system for other work, so the hire must run with little handholding after a roughly 6-week ramp. What You Inherit • Three supervised models on tabular data. A value model (CatBoost regression on expected spend), an intent model (survival model on time to first meeting), and an outreach model (calibrated contact-level classifier rolled up to the account). They blend into a 0-100 prospect score., • A weekly batch pipeline. Features are SQL on Snowflake. Models train and score on AWS SageMaker. dbt assembles point-in-time score history and applies eligibility rules., • A live business use. The score drives the quarterly assignment of prospect accounts to outbound reps' fixed books, and reps use it to prioritize within their books., • An explainable signal-tier layer. A daily tier that tells a rep which accounts are showing intent right now and names the reason. It is rolling out with RevOps., • An earlier handoff. Company has already shared a model validation dataset with PC's data science team., • Tools. Python, SQL, Snowflake, dbt, SageMaker, Hex, Salesforce, GitHub, Linear. What You'll Do • Run and maintain the scoring system. Own the weekly scoring runs, monitoring, data-quality fixes, and documentation. Support the lightweight November 2026 fixed-book refresh and the major January 2027 refresh., • Build the training data for PC's models. PC is building models for company's prospect targeting, trained on company data. This is the current project. Assemble and hand over the training sets: account history, Company-specific features, and outcome labels such as meetings, conversions, and spend. Make them point-in-time correct, with documented definitions, label rules, and known biases, including outreach that the existing score directed. Define labels the same way Company's own models do, so the two can be compared later., • Validate PC's scores on Company data. Test whether PC's scores give lift over Company's existing scores on Company outcomes. Keep the test honest by using accounts or time periods PC's models never trained on, usually out of time. Measure lift at the cutoff reps actually work, by segment, along with calibration and coverage. Give a clear recommendation to switch, blend, or hold. This read gates the cutover., • Lead the integration of PC's scores into Company. Data moves in both directions. PC will deliver its scores to Company, through a scheduled drop into Snowflake or through an API. Company may also need to send PC a recurring 5. 6. 7. 8. feed of Company-specific features for scoring, through a data share, a scheduled job, or a Company-side API. The interface is not designed yet. You lead the data science side end to end: requirements, feature definitions, the data contract with PC's team, validation of what comes back, monitoring, and cutover. Make sure incoming scores are stored with as-of dates so any past score can be reconstructed. Data Engineering and Company's engineering build the pipelines and any API. You write the data specs and validate the result., • Apply PC's scores in sales targeting. Start with PC's prospect risk score. With the technical lead, test whether it works better as an eligibility filter or as a ranking input, given how many accounts the books need. Implement the chosen path in the book-assignment tool behind a configuration switch. Design the control group for the January refresh and deliver the readout on approvals, conversion, and activation. The live test confirms what the offline validation predicts., • Be PC's working counterpart on scoring. Answer their data science team's questions about Company's models, data, and business. Keep Company's model documentation current. Field targeting-related data requests from PC and RevOps through the data team's PC request process., • Help get data approvals. Work with the hiring manager and the technical lead on Company's Legal, Compliance, and Trust reviews of data exchanged with PC, starting with the training data, and on model risk questions from PC. Write the use-case and data-handling summaries reviewers need, answer their questions, and track each approval to closure. So far this has been light work., • Project-manage the transition. Keep the plan, milestones, and decision log. Run the working cadence across PC, RevOps, and Data Engineering, and raise blockers early. Write the plan to retire Company's models and hand off what remains. Out of Scope • New model development. PC builds the replacement models. Retrains and fixes that keep Company's current models healthy are in scope., • ML engineering. Data Engineering and Company engineering build and own pipelines, APIs, serving infrastructure, and every data share with PC. You specify and validate., • Sales policy. RevOps decides book design and sales process. You supply the analysis., • Unapproved data use. Today, PC scores enter targeting as delivered attributes, not as training features in Company models. Every data exchange with PC, in either direction, goes through Company's approval path. That includes Legal review of which Company data can be shared. Requirements • 5+ years in data science, including owning production ML models end to end: build, deploy, monitor, retrain., • Supervised ML on tabular data, especially gradient-boosted trees. Survival or time-to-event modeling is a plus.-, • Strong SQL and Python. Snowflake and dbt preferred., • Has run batch scoring pipelines in production on SageMaker or an equivalent platform., • Has used an external partner's data or model scores in production, including data contracts, validation, and monitoring. Snowflake data sharing preferred., • Has built model training datasets: point-in-time features, label definitions, and leakage control., • Rigorous model evaluation: ranking metrics, calibration, point-in-time correctness, leakage, and bias from outreach that the score itself directed., • Has evaluated a vendor or partner model against an in-house baseline on held-out data., • Experiment design in an operating business: control groups, power, and readouts that decision-makers act on., • Lead or account scoring, sales targeting, or territory design in B2B, including Salesforce data., • Works directly with an external partner's technical team and with senior RevOps stakeholders. Writes clear documentation., • Runs a multi-team project without a dedicated project manager., • MS or PhD in a quantitative field, or equivalent production experience. Bonus • Fintech, card, or credit risk experience, especially using a third-party risk or credit score in targeting., • Has taken a data-sharing or model-use case through legal, compliance, privacy, or a bank's model risk review., • Experience with a feature store. Company uses Chalk. What We Screen For • Applied ML data scientist, not an ML engineer. Strong on modeling, evaluation, and getting model output used in the business. Engineering builds the infrastructure., • Ownership, not ticket execution. Figures out what the problem is and drives it without waiting for a spec., • Production depth. Has kept a model running in production and dealt with what broke. Research-only profiles don't fit., • Partner-facing judgment. Can tell an external team plainly what a model can andccan't support, and tell Company plainly when a partner's model doesn't beat ours.

Data Analyst / Data Scientist (Utility Analytics).Contract (1–2 Years, Special Project).We are seeking a detail-oriented.Customer Analytics and Digital Products team on a high-impact, special project.In this role, you will analyze large-scale utility datasets, evaluate customer-facing digital prod

Strong practical data science skills, including working with messy enterprise data and translating business problems into actionable analysis. Able to independently troubleshoot common data, query, environment, and pipeline issues — failed jobs, broken queries, bad joins, package conflicts, data-q

Assess data analysis, experimentation approaches, and data modeling techniques. Python, statistics, machine learning, and data analysis. Pandas, data modeling, and experimentation.

Strong experience in data visualization tools (e. Data Scientist (Live Anywhere). Perform Exploratory Data Analysis (EDA).

This role will work across both Data Science and Data Engineering, with a focus on analyzing operational data, developing performance benchmarks, maintaining dashboards, and troubleshooting vendor-facing data challenges. Data Science, Data Analytics, Analytics Engineering, Data Engineering, or a rel

Principal Data Scientist, Quantitative Intelligence (Consumer Transaction Data). They’re hiring a Principal Data Scientist to be the hands-on technical lead for the team that turns this data into statistical methods and machine learning models. Our client, a leader in consumer purchase data and an

Position: Data Scientist | AI & ML Analyst. Partner with analysts and stakeholders to solve business problems. Experience in Data Science, Machine Learning, Predictive Analytics, or Advanced Analytics.

Our client seeks a Principal Data Scientist to scale Data Science and AI capabilities for end-to-end payment optimization. Define technical vision and strategy for data-driven product initiatives focused on payment optimization, aligned to business goals. Foster a culture of innovation, experimentat

Our client, a major legal company, is actively looking for a Senior Data Scientist to join their team! This is a fully remote position, however candidates must be located in the US and able to work EST hours. At least 3 years of relevant experience in data science, machine learning, or NLP, includin

We're looking for a Junior Data Scientist to join a growing analytics team focused on building data-driven solutions and improving business performance through modern technologies. This role is ideal for someone who enjoys working with data, automation, and emerging AI tools in a collaborative, fast

As Scientist / Lead, Translational Data Platforms, you will play a pivotal role in shaping how researchers interact with complex clinical and multi-omic datasets, influencing platform strategy, developing innovative analytical capabilities, and helping build technologies that enable the next generat

About Sapio Sciences Sapio Sciences is on a mission to accelerate scientific drug discovery and high-throughput clinical and diagnostics for our clients and partners. The Sapio team consists of expert and highly collaborative scientists, software developers, and professionals passionate about providing a best-in-class lab informatics platform and industry-specific solutions. Sapio is one of the few software providers to offer a truly unified and highly configurable lab informatics platform and a broad suite of purpose-built solutions. The Sapio platform makes it easy for scientists, laboratory, and bio/informatics professionals to streamline and manage their end-to-end laboratory operations, from instrument data integration to workflow and experiment setup to sample and materials management, data management, and scientific data analysis and reporting. Working at Sapio At Sapio, we’re not just building lab informatics solutions. We’re creating tools made for the scientist and motivated to make the world a better place. We understand that lab informatics is about more than managing data or connecting workflows. It’s about making life easier for the scientist and making scientific progress faster for everyone. Our platform delivers the levels of configurability, usability and insight that scientists have only ever dreamed of. We’re a team of scientists, developers and innovators who question convention and stay focused on what matters most - advancing drug discovery science. We challenge the status quo and respond to the needs at the heart of science with powerful solutions that are simple to use, effortless to evolve and downright easy to love. As part of the Sapio team, you’ll work in a collaborative and forward-thinking environment where your ideas are valued, your growth matters and your work makes a difference. We’re proud to partner with leading labs around the world, from ambitious start-ups to global organisations, who trust Sapio to support discovery, development and diagnostics with industry-first science-aware solutions. About the Role As Head of Sales, you own the sales motion for Sigmatic Sciences end to end — carrying the revenue number and building the team, methodology, and pipeline that deliver it. This is a global mandate weighted toward the United States and European markets, spanning new business, expansion, and retention across our Strategic Account Managers and Scientific Liaisons. You will define how Sigmatic sells: the account planning, engagement model, and playbooks that make growth repeatable and forecastable. You will lead from the front — coaching the team while personally driving the most complex, highest-value deals. Marketing, Product, and Customer Success are your closest partners, and you will work in lockstep with them to convert demand, shape the roadmap, and secure long-term customer value. Sales execution, and the number, are yours. This is a pivotal hire. You will build the growth engine that scales Sigmatic globally, turning a genuinely differentiated platform into commercial momentum in a market that is only beginning to grasp what lab-in-the- loop can do. Working Location This is a remote role based on the East Coast of the United States, with regular travel to meet your global team, key customers, and Sapio leadership. Our people are distributed worldwide, with physical offices in York, PA; Fort Lauderdale; and London. Responsibilities About the Role • Own the number. Carry and deliver Sigmatic's revenue targets across new business, expansion, and net revenue retention and take personal responsibility for the strategic accounts that anchor the annual plan., • Define how Sigmatic sells. Establish the account-planning methodology, engagement cadences, and expansion playbooks that make growth predictable, with Scientific Liaisons deployed to lead business-value conversations at the core of the go-to-market model., • Run a disciplined operating rhythm. Own forecasting, pipeline hygiene, CRM discipline, and a visible, relentlessly managed set of KPIs that underpin dependable performance., • Own executive relationships. Serve as the senior point of partnership and escalation on your most important and most complex accounts., • Partner with leadership. Work with the CCO and wider leadership to set targets, shape priorities, and report on the health of the sales portfolio., • Collaborate across the go-to-market team. Work closely with Marketing, Product, and Customer Success — converting demand into pipeline, feeding customer and market intelligence into the roadmap, and aligning on retention and expansion — without owning those functions., • Represent Sapio and Sigmatic. Show up with credibility at industry events, customer meetings, and executive briefings as a senior commercial leader. Requirements • A track record of leading enterprise B2B SaaS sales and consistently meeting or beating revenue, net revenue retention, and expansion targets in complex, multi-stakeholder accounts., • Experience building and scaling a distributed sales or presales team across multiple geographies and cultures., • Fluency in a recognized enterprise sales methodology (e.g., MEDDIC, Challenger, Force Management), with strong command of executive-level engagement and multi-year commercial negotiation., • The ability to build the playbooks, cadences, and account plans a scaling team runs on — and to lead from the front on the hardest deals., • Enough command of agentic AI, lab-in-the-loop, and in-silico approaches to sell their value credibly to scientific and executive buyers in therapy design and development., • Experience selling into life sciences, biopharma, or diagnostics strongly preferred; familiarity with lab informatics and an existing network in pharma or biotech are distinct advantages. Required Skills & Attributes • Exceptional people leadership — you inspire, develop, and hold a distributed team accountable to a high bar, • Strong executive presence and the credibility to engage C-suite and senior stakeholders as a peer, • Commercial acumen across account planning, renewals, and expansion, with a data-driven approach to, • forecasting and performance, • Clear, persuasive communication that builds trust quickly, both internally and with customers, • A collaborative operator who influences without authority and thrives cross-functionally, • Comfortable in a fast-paced scale-up where you build structure as you grow, • Willing to travel regularly across the US and to Europe to support your team and key accounts Our People At Sapio Sciences, our people are at the heart of everything we do. Our lab informatics solutions are powered not just by technology, but by a shared commitment to our core values—what we proudly call The Sapio Seven, aka EMBRACE: • Embrace the future • Make a difference • Be true partners • Remember why we’re here • Act with empathy • Commit to transparency • Excel in quality These values shape how we work, how we grow, and how we deliver impact for our clients every day. Reasonable Adjustments We are committed to ensuring an inclusive and accessible recruitment process. If you require any reasonable adjustments, at any stage across the application, interview & assessment processes then please let us know. We will work with you to support your needs in confidence. Diversity, Equity & Inclusion We do not discriminate based on race, colour, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, disability, or any other status protected by law or regulation. We intend that all qualified applicants be given equal opportunity and that selection decisions are based on job-related factors. We are committed to creating a diverse and inclusive workplace and encourage applications from all qualified individuals. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Type: Temp (6–9 months) • Location: Remote US TZs Target Roles: Forward Deployed Engineer, Senior ML Engineer, Research Engineer/Scientist (Alignment), AI Product Manager Own the top of funnel: map the market, generate calibrated pipelines fast, and hand off HM-ready prospects with crisp notes. No “post & pray.” What You’ll Do (Sourcing-First) • Market mapping: Build company/lab/OSS target lists; track movers (arXiv, OpenReview, Google Scholar, Hugging Face, GitHub)., • Outbound at scale: Tailored outreaches; iterate subject/copy; test 3–5 variants; maintain reply metrics., • Profile calibration: Validate signals (repos, papers, shipped features, MLOps stack, product outcomes)., • Light pre-screen (10–15 min): Confirm level, comp ballpark, work auth, location, timeline, and role fit; capture structured notes., • Handoff: Package candidates with scorecard snapshot + links; book with recruiter/HM. Role Signals You’ll Target • FDE: Customer-facing build/ship, Python + data wrangling, rapid proto, consultative comms., • Senior ML Eng: Prod LLM/ML (inference, RAG, fine-tune, evals), MLOps (deploy/observability)., • Research/Alignment: Strong math/ML fundamentals; safety/evals/interpretability/RLHF artifacts., • AI PM: Shipped AI features, experiment cadence, metric/latency/trade-off literacy. Must-Haves • 2+ years sourcing for deep eng/ML/AI roles, • Tools: ATS, LinkedIn Recruiter, GitHub search, arXiv/Scholar, Notion/Airtable, Gem/Fetcher., • Clean pipeline habits Engagement • 30–40 hrs/week

We are looking for a Staff Data Scientist to join our Safety Insights Data Science team. In this role, you will partner closely with Safety Product, Engineering, and Machine Learning teams along with fellow Safety data scientists, to shape strategy, product development, define and evolve safety metr

Mentor and guide junior data scientists, fostering technical excellence and continuous learning within the team. Optimize and automate data pipelines using Python, PySpark, and R, ensuring efficient data processing and feature engineering. Expertise in data pipeline automation and orchestration usin

We are seeking an experienced AI Data Scientist to join Deltek’s growing AI Center of Excellence team to design, build, evaluate, and optimize enterprise AI solutions leveraging Large Language Models (LLMs), AI Agents, Retrieval-Augmented Generation (RAG), multimodal AI, Opensource AI Models, and

Leverage Verasight’s data pipelines and processing procedures to own the data collection and quality process from end to end, including: building contact lists and sampling frames, monitoring survey fielding, cleaning and analyzing survey response data, writing methodology documentation, and prepa

We are seeking a Data Scientist, Associate to build RoboStrategy’s data capability from the ground up. Bring together shareholder records, 13F filings, DTC position reports and allocator databases into a single view connected to our CRM, and use it to score, segment and prioritize investors, handl

Perform deep EDA, data profiling, and quality audits to ensure model-ready data standards. Identify, collect, clean, validate, and transform all data required for prediction model consumption. Core Data Science & Mathematics.

We're hiring a Remote Data Scientist for a fast-growing, well-funded fintech company. This is a hands-on, non-client-facing seat on a small data team - you'll apply Python, SQL, and applied statistics/ML to real product and risk questions, working closely with engineers and analysts, and presenting

Principal Data Scientist - Quantitative Intelligence. ML Scientist, Statistical Modeling, Predictive Modeling, Paneling, Normalization, Cohorting, Causal Inference, Synthetic Control, Uplift Modeling, Transaction Data, Behavioral Modeling, Forecasting, Representation Learning, Privacy Preserving ML,

Working closely with Marketing, Product, Finance, and Technology, the Senior Data Scientist transforms complex data into actionable insights that improve business performance in a highly competitive telecommunications environment. The Senior Data Scientist, Advanced Analytics, develops machine learn

Coordinates with Data Engineers to build data environments providing data identified by Data Analysts, Data Integrators, Knowledge Managers, and Intel Analysts. Defines company data assets (data models), spark, sparkSQL, and hiveSQL jobs to populate data models. Data Scientist Job Duties: Formulates

Director, Advanced Data Scientist will provide senior strategic and technical leadership within the Partnership Division's data science and modeling function. Develop innovative approaches for extracting predictive signals from traditional and alternative data sources. The Ideal Candidate for This R

Coordinates with Data Engineers to build data environments providing data identified by Data Analysts, Data Integrators, Knowledge Managers, and Intel Analysts. Defines company data assets (data models), spark, sparkSQL, and hiveSQL jobs to populate data models. Data Scientist Job Duties: Formulates

The role involves performing medical reviews and data analyses, managing project scope, and identifying risks related to data integrity. Syneos Health/ inVentiv Health Commercial LLC is seeking a Clinical Scientist - Respiratory to collaborate with Medical Directors on developing medical plans and e

You'll collaborate directly with an experienced data science team and advisors from top research institutions while helping develop production-grade causal inference models used by enterprise customers. Python and modern data science tooling. Machine Learning & Data Science Tools.

Outlier Experts work on a variety of projects, from generating training data to evaluating the performance of models. Additional incentives data is based on payments made in the past six months and is updated quarterly.

Product Testers are wanted to work from home nationwide in the US to fulfill upcoming contracts with national and international companies.We guarantee 15-25 hours per week with an hourly pay of between $25/hr.There is no payment required in order to apply or to work as an In-Home Usage Tester.You do

GDA Contractors is a specialty construction firm providing waterproofing, caulking, and moisture protection and restoration services to general contractors and building owners.With a highly trained, service, and safety-conscious staff, our objective is to perform our contracted work in a manner that

ENR Top 400 – Top Data Center Contractor (Top 3).

Waco, TX and Hammond, LA Offices.Our experience and expertise cover a wide range of below-grade and above-grade construction for High Voltage Electrical Substations and Transmission Lines.We operate in the power, industrial, oil & gas, and chemical markets.Due to continued growth, Bobcat Power is se