Wholesale Credit Quantitative Research - Senior Associate

JPMorganChase is a leading global financial services firm. Within Risk Management and Compliance, we help keep the firm strong and resilient by anticipating emerging risks and delivering best-in-class analytics. As an Associate in Wholesale Credit Risk Quantitative Research, you will support the development and maintenance of credit risk models used in firmwide stress testing and loss forecasting frameworks, including CCAR, CECL, and IFRS 9. You will work with large-scale financial and macroeconomic datasets and partner with model risk governance/review, technology, data, finance, and lines of business stakeholders to deliver well-controlled, production-ready solutions. The position requires the knowledge/experience in quantitative modeling, preferably in wholesale credit risk models such as probability of default (PD), loss given default(LGD) and exposure at default (EAD) models. Job Responsibilities Perform basic credit risk modeling both at single-obligor level and portfolio level. Support research, development, and ongoing enhancement of PD forecasting and rating migration models Analyze large financial/credit/macro datasets (panel and time-series), including data cleaning and feature construction Perform model testing, monitoring, diagnostics, and validation support analyses Partner with technology teams to maintain model libraries following the organization’s software development standards Prepare clear technical documentation and support internal governance and regulatory review requests Apply appropriate, interpretable AI/ML methods where suitable while maintaining strong controls and explainability Required qualifications, capabilities and skills Master’s or Ph.D. in a quantitative discipline (e.g., Statistics, Econometrics, Economics, Math, Engineering, Computer Science, Finance, Physics) 1 plus years of relevant experience. Strong foundation in statistical/econometric modeling (e.g., GLMs, logistic regression, time-series forecasting; clustering, decision trees) Experience working with large datasets (especially panel/time-series) and strong data analysis skills Hands on Python programming Strong problem-solving, communication, and ability to manage multiple workstreams Experience producing technical documentation (including for governance/review) Preferred Qualifications Exposure to wholesale credit risk and/or credit risk modeling (PD/LGD/EAD modeling strongly preferred) Experience with CCAR, CECL, and/or IFRS 9 modeling environments Knowledge of numerical methods (optimization/root finding) and/or large-scale computation (e.g., Monte Carlo, multi-core/HPC) Experience using AI/ML methods in risk contexts with attention to interpretability and governance