Top Machine Learning Jobs in Philadelphia, PA

Lead design and delivery of reusable, AI-ready data products and interoperable data-engineering artifacts for retail-banking portfolios. Manage data product lifecycles, ensure data integrity and governance, create self-service analytics, and align technology and business teams in a matrixed organization.

As Head of Investment Data, you will define and drive data strategy, governance, and operations to enhance investment decision-making and client outcomes.

Lead and architect high-value healthcare analytics engagements, build reusable analytic frameworks (DBT), partner with product and engineering, mentor analysts, ensure analytic quality, and represent Arcadia externally to drive population health and value-based care outcomes.

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.

Support risk and analytics efforts by performing SQL/Python-based analysis, monitoring portfolio health, building scalable reports, collaborating cross-functionally, and integrating AI tools to surface insights that inform product, risk, and growth decisions.

Designs, builds, and maintains scalable ETL pipelines and databases; integrates large-scale data for analytics; develops and tests code, queries, and reports; ensures data quality and governance; leads solution development, trains users, and supports ML and BI initiatives.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

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.

Perform quantitative research for Agency and commercial MBS, applying machine learning and alternative data to enhance investment decision-making. Partner with portfolio managers and traders to build and improve models, create new measures, and communicate data-driven insights to inform active fixed income strategies.

Design and implement data integration and data warehouse solutions using Big Data and open-source technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and ML concepts, and implement real-time streaming and aggregation patterns. Hands-on SQL/NoSQL and programming experience required.

Design and implement data integration and data warehouse solutions using big data technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and machine learning knowledge, and implement real-time streaming and aggregation patterns.

Lead fraud strategy and real-time decisioning for payment channels, design and tune rules engines, drive platform builds with engineering, use analytics and models to optimize detection and reduce false positives, partner cross-functionally, influence senior stakeholders, and build a high-performing fraud analytics team while ensuring governance and regulatory compliance.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Analyze large federal and state economic datasets, automate data collection and scraping, and rapidly publish clear, data-driven reporting on market-moving indicators. Collaborate with reporters and visual journalists, file public-records requests, and use emerging technologies including AI to enhance real-time coverage.

The Statistical Programmer analyzes large datasets using SAS and SQL, and applies various statistical methods such as regression and multivariate analysis, while collaborating effectively with team members.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Lead strategy and execution to prevent, detect, and disrupt digital fraud, scams, and money mule activity. Build and mentor data science and analytics teams, develop ML predictive models, partner with product, cyber, vendors, and risk teams, and drive prevention-first controls, compliance, and operational efficiencies across payments, onboarding, and authentication channels.

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.

Design and scale fraud-mitigation strategies for buyer, seller, and account fraud across Stripe products. Monitor portfolios, identify and predict risky behavior, propose controls, automate workflows, collaborate with Product/Engineering/Data Science, and educate users to minimize losses while enabling 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 combined Analytics Engineering and Data Engineering teams owning the end-to-end data roadmap from ingestion to trusted metrics. Manage hiring and performance, set architecture and platform investments, enforce testing/monitoring/documentation standards, review technical decisions, and partner with product and business stakeholders to translate needs into prioritized deliverables.

Analyze historical demand, occupancy, transactions and competitor pricing to optimize daily, monthly, and event pricing. Build dashboards, deliver revenue insights to leadership, and collaborate with Operations and Marketing to implement pricing and promotional strategies while maintaining data integrity.

Manage end-to-end catastrophe modeling projects, support client discussions on reinsurance strategy and portfolio management, maintain stakeholder relationships, develop region/peril expertise, perform data management and technical analysis, and lead internal research and automation initiatives.

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