Top Fintech Data & Analytics Jobs in Philadelphia, PA

The Portfolio Architect will enhance investment platforms and tools, partners with portfolio managers, and implement analytics for liquidity management and trading.

Lead Toast's credit risk strategy, overseeing portfolio management, product growth, and analytics while ensuring compliance with regulations and mentoring analysts.

Manage analytical service operations by overseeing a team, designing software for data mining, developing statistical models, and generating insights for marketing outcomes. Provide training and mentorship to direct reports.

The Director of Analytics leads Upstart's Credit Analytics team, driving credit forecasting, valuation, and risk capital. Responsibilities include setting vision, developing leaders, and enhancing analytical frameworks in collaboration with tech and finance teams.

Support sales intelligence by collecting, preparing, and analyzing sales and marketing data; maintain dashboards and data quality; run pipeline and win/loss analyses; translate findings into clear visualizations and reports for stakeholders.

The Senior Data Engineer II will design and maintain scalable data pipelines, collaborate with teams on machine learning workflows, and ensure data accessibility across systems.

The Market Risk Analyst will monitor SBL lines of credit, assess market risks, manage collateral, develop risk models, and prepare reports.

Design, develop, and maintain forecasting and optimization models for contact forecasting, headcount planning, staffing and budget. Partner cross-functionally to produce data-driven recommendations, present to leadership, and mentor forecasting analysts.

As a Staff Analytics Engineer, you will bridge data engineering, data science, and business analytics to generate actionable insights, build data models, and empower stakeholders across the organization.

Lead end-to-end data science initiatives: design, deploy, and monitor production ML models for credit risk, pricing, collections, and fraud. Partner cross-functionally to integrate models, set development standards, mentor team members, and maintain reliable, scalable machine learning pipelines and monitoring systems.