Role: Manager - Credit Risk Strategy Location: Bay Area, CA Type: Hybrid (2-3 days in office / week) Responsibilities: As a Risk Strategy professional, you will design and execute data-driven financial risk and fraud strategies across money movement products. You will own the end-to-end policy lifecycle- from hypothesis and testing to deployment and performance monitoring-using large-scale data to balance risk mitigation with business growth. You will collaborate with cross-functional teams to build scalable solutions and respond to critical risk events. • Support financial risk and fraud aspects of business initiatives, including responding to high-severity and time-sensitive risk incidents • Apply industry knowledge, statistical modelling, 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 to production, and monitor performance • Build expertise across risk types in money movement products, 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 limits, eligibility criteria, and segmentation frameworks • Monitor portfolio trends, including 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 modelling 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 Candidate Profile: • Strong experience in risk strategy, credit policy, underwriting, fraud or financial analytics • Hands-on experience with large datasets and analytical problem-solving • 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, fast-paced environments Preferred Qualifications: • Bachelor's degree in quantitative fields such as Data Science, Statistics, Mathematics, Economics, Finance, or Engineering • Master's degree in a related quantitative discipline is a plus • Experience in financial services, fintech, or risk management domains