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Job Description
Manager•
Credit Risk Strategy Location:
SF Bay Area, CA (Hybrid•2-3 days/week in office)
Experience:
8-12
Years Employment Type:
Full-Time Role (No Contract) About the
Role:
We are seeking a Manager•Credit Risk Strategy with strong Banking or FinTech analytics experience to design and execute data-driven financial risk and fraud strategies across money movement products. This role requires ownership of the end-to-end policy lifecycle, from hypothesis development and testing to deployment and performance monitoring, using large-scale data to balance risk mitigation with business growth objectives. The ideal candidate will bring expertise in credit risk strategy, underwriting, fraud analytics, portfolio management, and risk policy development, along with strong analytical and stakeholder management skills.
Key Responsibilities:
Support financial risk and fraud aspects of business initiatives, including responding to high-severity and time-sensitive risk incidents. Apply industry knowledge, statistical modeling, and analytics to develop practical risk strategies using large-scale transactional and account-level data. Own the full lifecycle of risk strategy and policy development: Identify opportunities Define action plans Test policies Deploy strategies to production Monitor performance and optimize outcomes Build expertise across risk types in money movement products while balancing risk mitigation with business growth objectives. Partner with Data Science, Risk Operations, Product, Data Engineering, and Analytics teams to design segmentation strategies and portfolio analyses. Develop and implement underwriting strategies, including credit limits, eligibility criteria, and segmentation frameworks. Monitor portfolio trends, concentration risks, and segment-level performance. Key Business Problems /
Use Cases:
Underwriting, credit limits, and eligibility-based decisioning. Portfolio monitoring, including segmentation, trend analysis, and concentration risk assessment. Financial loss forecasting and behavioral modeling using payments, card/ACH, and account-level data. Hypothesis-driven analysis to improve risk strategies and customer outcomes.
End-to-end policy lifecycle management:
Design → Test → Launch → Monitor → Iterate.
Required Qualifications:
Strong experience in risk strategy, credit policy, underwriting, fraud, or financial risk analytics. Hands-on experience working with large datasets and solving complex analytical problems. Proficiency in SQL and Python for data analysis and model implementation. Experience in statistical modeling, forecasting, or risk analytics. Ability to translate business problems into data-driven solutions. Strong communication and stakeholder management skills. Experience working in cross-functional and fast-paced environments. Banking or FinTech industry analytics experience is required. 3-6+ years of relevant experience in fraud, credit, or financial risk analytics.
Preferred Qualifications:
Bachelor s degree in Data Science, Statistics, Mathematics, Economics, Finance, Engineering, or another quantitative discipline. Master s degree in a related quantitative field is a plus. Experience within Financial Services, FinTech, Banking, Payments, Lending, or Risk Management domains.
