Senior Machine Learning Engineer
hace 13 días
Philadelphia
Job DescriptionSenior Machine Learning Engineer Location: Philadelphia, PA Onsite Flexibility: Onsite Job Details • Position Type: Right to Hire (Contract-to-Hire), • Pay / Salary: $160,000 / Year (USD), • Work Authorization: Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.Job Summary We are looking for a Senior Machine Learning Engineer to work hands-on on machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role is focused on building, validating, deploying, and improving machine learning models as a strong individual contributor, working alongside senior technical leadership who will help shape problem definition and overall model strategy. This is a hands-on model-building role. The ideal candidate should be comfortable spending most of their time working directly with data, features, models, scoring logic, validation methods, production workflows, and model improvement and should bring solid engineering judgment, ownership of their deliverables, and the ability to drive their work forward without waiting for perfect requirements. We are especially interested in candidates with experience building predictive scores, risk scores, health scores, engagement scores, prioritization models, or similar decision-support systems. Experience with transparent, interpretable, and explainable models is valuable, especially in environments where business trust, auditability, and operational adoption matter. This is a fast-moving, startup-like environment. Requirements may be incomplete and priorities may evolve; the right candidate is comfortable iterating quickly and helping create clarity within their own workstream. A background in commercial software, SaaS, digital products, fintech, healthtech, consumer technology, or other product-driven environments is preferred. Experience with Generative AI is also useful, especially where LLMs, RAG, summarization, conversational AI, agents, document intelligence, or AI-enabled workflow automation can complement traditional predictive models and scoring systems. The Top Three Things We Are Looking For 1. Strong Hands-On Production ML Builder The right candidate must be able to personally build models. They should be comfortable taking messy data and a defined prediction problem and turning it into a working, validated, usable model including feature engineering, model training, validation, calibration, thresholding, monitoring, production scoring, and model improvement., 2. Product and Commercial Software Mindset The right candidate should think beyond model performance and understand how models become useful product capabilities: who will use the model, what decision it supports, what action it should trigger, and how success will be measured. Experience in commercial software, SaaS, fintech, healthtech, consumer products, fraud, credit, pricing, personalization, or similar product-driven environments is valuable., 3. Experience with Transparent Scoring and Decisioning Because this role supports scores and decisioning systems, we value candidates who have built models that people can understand, trust, monitor, and act on scorecards, risk scores, health scores, calibrated models, score bands, thresholds, and business-facing model explanations. Strong candidates without direct scorecard experience but with solid interpretable-modeling fundamentals will also be considered. Key Responsibilities Hands-On Model Development • Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, and decision support., • Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation., • Develop practical models that balance predictive performance, explainability, stability, maintainability, and business usefulness., • Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features., • Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries., • Move quickly from data exploration to prototype to validated model to production-ready capability. Scoring and Transparent Models • Implement predictive scores, risk tiers, score bands, thresholds, cut points, and intervention logic based on agreed designs., • Build transparent and interpretable models where explainability matters, including logistic regression, GLMs, decision trees, calibrated models, or explainable boosting approaches., • Evaluate models for accuracy, calibration, stability, drift, and operational usefulness., • Document model logic, features, assumptions, limitations, and validation results in a way that business and technical stakeholders can understand. Production ML and MLOps • Partner with data engineering, platform engineering, and application engineering teams to move models from experimentation into reliable production workflows., • Support model deployment, batch scoring, real-time or near-real-time inference, model versioning, monitoring, retraining, and performance tracking., • Expose models as well-documented services/APIs consumable by application teams; familiarity integrating ML capabilities into .NET/TypeScript-based products on Azure is a plus., • Ensure models are observable, supportable, secure, and aligned with architecture and governance expectations. Product and Rapid-Build Execution • Operate effectively in a rapid-build, startup-like environment where speed, ownership, and pragmatic decision-making matter., • Turn defined business needs and rough concepts into working ML prototypes and production capabilities, iterating based on feedback., • Make smart tradeoffs between quick prototypes, transparent models, GenAI-enabled workflows, and longer-term maintainability, with guidance from technical leadership. Generative AI and AI Automation • Contribute to GenAI-enabled solutions, including LLM-powered workflows, RAG, summarization, conversational agents, and document intelligence., • Help evaluate when GenAI is appropriate versus traditional ML, rules, analytics, or transparent scoring models., • Apply appropriate evaluation, guardrails, monitoring, privacy controls, and human-in-the-loop processes for GenAI use cases. Stakeholder Collaboration • Work with business, product, analytics, and engineering stakeholders to clarify what a model is intended to predict, explain, recommend, or trigger., • Translate business questions into measurable ML objectives, target variables, features, validation approaches, and success metrics, with support from senior technical leadership., • Communicate model behavior, tradeoffs, limitations, and recommended usage clearly to both technical and non-technical audiences., • Participate in code reviews and design reviews, and contribute to team standards for model development, validation, documentation, and production readiness.Required Experience, • 5 years of professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields., • 3 years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration., • 2 years of experience deploying, operationalizing, or supporting models in production or business-critical environments.Nice-to-Have Experience, • 7 years of relevant professional experience in ML, data science, applied AI, or production analytics., • Experience building scorecards, risk scores, health scores, engagement scores, churn scores, fraud scores, or operational decision-support models., • Experience with transparent or interpretable models such as logistic regression, GLMs, GAMs, decision trees, calibrated models, or Explainable Boosting Machines., • Experience in commercial software, SaaS, digital products, fintech, healthtech, consumer technology, or other product-driven environments., • Experience in startup, scale-up, or rapid-build environments requiring independent execution amid ambiguity., • Experience with GenAI, LLMs, RAG, AI agents, prompt engineering, model evaluation, or AI-enabled workflow automation., • Experience in healthcare, population health, remote patient monitoring, insurance, financial services, or other domains where model trust and explainability are important., • Experience with MLOps practices including model registries, deployment pipelines, monitoring, drift detection, and retraining strategies., • Experience delivering ML within an Azure-centric application environment (.NET / TypeScript services), or supporting teams through a platform modernization.Required Skills, • Strong hands-on experience with Python and SQL., • Experience with modern ML and data platforms, with Azure strongly preferred (Azure ML, Azure Databricks, Spark, MLflow, Snowflake, or similar)., • Solid understanding of model evaluation, calibration, thresholding, monitoring, drift, retraining, and the production ML lifecycle., • Ability to explain model behavior, performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders., • Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability., • Ability to work independently as a hands-on senior contributor within a defined workstream.Work Environment / Physical Requirements, • Ideal candidate traits include:, • Hands-on: comfortable spending most of their time building, testing, validating, and improving models. - Practical: knows when a simple transparent model is better than a complex one. - Product-minded: understands that models need to improve decisions, workflows, outcomes, or business results. - High ownership: identifies what needs to be done within their workstream and drives it forward. - Startup comfortable: can operate with evolving priorities, imperfect information, and rapid iteration. - Evidence-driven: wants to prove that a model is valid, stable, explainable, actionable, and useful. - Technically rigorous: cares about data quality, calibration, monitoring, reproducibility, and production readiness. - Collaborative: works well with business, product, analytics, engineering, and operations stakeholders. - Clear communicator: can explain model design and tradeoffs without hiding behind jargon., • Success in this role looks like:, • High-quality models and scores are built, validated, deployed, monitored, and improved over time. - Model outputs are explainable and trusted by business and operational stakeholders. - Scores are connected to real decisions, workflows, interventions, or measurable outcomes. - Models ship quickly, iterate based on feedback, and mature from prototype to production without over-engineering. - Work is documented, reproducible, and production-ready, meeting team standards for model development and monitoring. Benefits • Medical, Vision, and Dental Insurance Plans, • 401k Retirement FundAbout GTT GTT is a minority-owned staffing firm and a subsidiary of Chenega Corporation, a Native American-owned company in Alaska. We highly value diverse and inclusive workplaces and support Fortune 500 organizations across banking, financial services, technology, life sciences, biotech, utilities, and retail sectors throughout the U.S. and Canada. Job Number: 26-11393 Industry: Manufacturing & Operations #LI-Hybrid #LI-GTT #gttmf
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