Top Senior Machine Learning Jobs in Philadelphia, PA

Partner with Total Rewards stakeholders to design and execute analytics that evaluate employee wellbeing and benefits program ROI. Combine and validate data from claims, utilization, and other sources, build forecasting and advanced-analytics models, and deliver dashboards and executive summaries. Lead project workstreams, mentor junior analysts, and ensure data quality, compliance, and actionable insights to inform benefits strategy.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Leads the Underwriting Data Engineering team and oversees the design, development, and productionization of scalable data and machine learning pipelines. Responsibilities include feature engineering, data preparation, model deployment, data validation, governance, monitoring, API integration, analytic data products, roadmap development, stakeholder collaboration, and team coaching.

Leads business analysis, advanced analytics, KPI development, data modeling, reporting automation, and AI/ML initiatives supporting global data-center construction and operational excellence. Builds datasets, dashboards, performance metrics, forecasts, and scenario models; translates complex findings for executives; aligns technology solutions with business needs; and promotes data governance, documentation, process improvement, and analytics maturity across cross-functional teams.

Manage enterprise metadata, business glossaries, data catalogs, and end-to-end data lineage for a federal case management modernization program. Configure governance and metadata platforms, integrate data sources and pipelines, establish standards and workflows, monitor metadata quality, support compliant data sharing, develop governance metrics, document procedures, and train data stewards and business users.

Lead partner analytics by building scalable dashboards, forecasting and ROI models, and experimental designs to evaluate and optimize partner performance. Collaborate with Product, BD, Marketing, and Data Engineering to improve pipelines, translate insights into recommendations, and mentor analysts to scale partner growth.

Lead end-to-end analytics for Flagship Client and Distribution: partner with stakeholders to frame problems, perform advanced analyses and machine learning, ensure data quality, develop recurring analytics products and executive visualizations, and mentor analysts to drive data-driven decisions.

Lead and develop a team of partnership analysts; translate business questions into analytical frameworks, experiments, forecasts, and ROI models. Build dashboards and reporting infrastructure, analyze cohorts, funnels, retention, and partner performance, and own measurement frameworks for growth initiatives. Partner with Business Development, Engineering, Product, and Marketing to shape partnership strategy and communicate data-driven insights to technical and non-technical leaders.

Designs, develops, deploys, maintains, and optimizes ETL processes and cloud-based system integrations for healthcare data warehousing and business intelligence. Works with DHA and Cerner inpatient and outpatient data, develops database procedures, validates migrations, performs data mapping and modeling, and supports data science and machine learning teams. The role also documents processes, tunes performance, captures client requirements, and manages data transformation initiatives.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

Leads the strategy, architecture, and execution of an enterprise data platform supporting analytics, machine learning, operational systems, and AI applications. Manages and mentors data platform engineers while remaining hands-on with architecture, reliability, technical direction, and troubleshooting. Oversees pipelines, orchestration, governance, security, observability, data contracts, migrations, and platform operations. Partners cross-functionally to translate business needs into scalable technical solutions and communicates decisions to technical and non-technical stakeholders.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Lead analytics partner to Community Support, driving data-driven roadmaps, scalable reporting and measurement, causal inference and experimentation, and improvements to agent/guest/host experiences via analytics, modeling, and stakeholder influence.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead development of advanced analytical and machine learning models to optimize forecasting, inventory placement, and supply chain flows. Automate reports, analyze large datasets, deliver insights to leaders, drive cross-functional initiatives, mentor junior analysts, and recommend process improvements to boost operational performance.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

Lead design, build, and operation of data architecture, pipelines, and semantic layers. Manage sprint planning, DevOps metrics, data quality and monitoring, and MLOps enablement. Partner with stakeholders, coach engineers, track project plans, and ensure data availability, accuracy, and documentation.

The Manager of RevOps/Yield analyzes patterns and data to optimize ad operations, manage vendor relationships, and oversee a remote team for revenue generation.