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