Manager

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