Top 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.

As a Senior Associate in the Financial Crime Unit at PwC, you'll analyze complex datasets using SQL and Python, apply machine learning techniques, mentor junior team members, and build client relationships while enhancing your technical skills.

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

This role involves applying advanced data science and machine learning methods to healthcare data, leading teams, managing client relations, and developing analytics products for payers, while mentoring junior staff.

The Analytics Lead will leverage data analytics to optimize credit strategies, monitor portfolio performance, and collaborate on credit policy with stakeholders across technical and non-technical teams.

Lead complex analytics projects supporting client experience transformation: gather and validate structured and unstructured data, apply advanced analytics and machine learning, produce executive visualizations and recommendations, manage recurring reporting, enforce data quality standards, and mentor other analysts to drive business decisions and improve client outcomes.

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.

Use advanced analytics and ML to design and monitor credit risk models, optimize underwriting and credit strategies, partner with Engineering/Product/Compliance, oversee portfolio performance and regulatory alignment, and lead cross-functional initiatives to balance growth, compliance, and risk mitigation.

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.

Perform data collection, cleaning, validation, and analysis on large client datasets using Python, SQL, and spreadsheets. Produce actionable insights and business narratives, automate data workflows, and support text/document annotation to generate labeled data for NLP and LLM solutions. Collaborate with senior analysts and improve internal analytical processes and tool configurations.

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.

Join Nextdoor's machine learning team to build data-intensive products, develop ML models, analyze datasets, and enhance user experiences. Collaborate closely with product and data science teams.

The Director of Statistics leads statistical initiatives in phase 3b/4 and RWE studies, provides expertise in HTA and regulatory compliance, and collaborates with cross-functional teams. Responsibilities include statistical analysis plans, Bayesian methodologies, and mentoring junior staff.

The role involves designing and implementing advanced analytics solutions for healthcare payers, analyzing complex problems, mentoring juniors, and building client relationships.

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

Lead client-facing Data & Analytics engagements across pre-sales, architecture, and delivery. Define strategy, roadmaps, solution designs, and SOWs for modern data platforms, pipelines, governance, analytics, and AI enablement. Drive practice development, create reusable IP, mentor teams, and ensure quality delivery and strong business outcomes.

Lead a team of analysts to advance fraud and safety operations through analytics, ensuring robust data products and compliance while empowering stakeholders with actionable insights.

Lead design, implementation, and scaling of enterprise Palantir data solutions. Oversee cross-functional teams to build data pipelines, integrations, governance, and advanced analytics (ML/AI). Manage project delivery, stakeholder engagement, performance optimization, and mentor senior engineers and analysts while ensuring security and regulatory compliance.

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.

As a Senior Impact Analyst, you will analyze sustainability challenges, design and manage interventions, and work closely with smallholder communities, emphasizing rigorous research and collaboration.

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.

As an Associate Engineer in Data Science, you will leverage data analysis and machine learning to solve complex problems and develop inspiring digital products.

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.