Top Fintech Data & Analytics Jobs in Philadelphia, PA

The role involves managing actuarial modeling for Retirement Plans, ensuring quality of outputs, overseeing BAU execution, and training junior staff.

The Senior Pricing and Paywalls Product Analyst will automate analytical data collection, analyze product metrics, organize A/B tests, and support the generation of product hypotheses to identify growth opportunities.

The Fraud Analyst monitors transactions for suspicious activity, uses fraud detection tools, documents findings, communicates with clients, and supports the Risk & Compliance team.

The Senior DataOps Engineer oversees data management and storage systems, ensuring timely access to secure and accurate data, improving data workflow automation, and collaborating with teams to meet business needs.

The Strategy Analyst provides analysis and strategic recommendations to enhance sales, underwriting, marketing, and operations, leveraging data to drive business growth and improve operational efficiency.

The Sector Lead for Financial Services will oversee channel research projects, ensuring quality and relevance while guiding a team of analysts. Responsibilities include developing questionnaires, managing research workflows, and expanding coverage across financial services sectors.

The Principal Credit Risk & Pricing Analyst will perform statistical analysis, develop credit strategies, communicate recommendations, and collaborate with teams to optimize credit policy and enhance portfolio management in real estate lending.

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

Lead the design, development, and management of Workday integrations and Extend applications; oversee integration projects and ensure compliance with data security standards.

Build and own the reporting and insight layer: write optimized Redshift queries, build dashboards, monitor model feedback signals, triage and prioritize investigations for data science, maintain ETL pipelines ingesting semi-structured data, and enable Customer Success with clear metrics and onboarding checks. Move into model validation and automation to scale analytic workflows.