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

Skills
Python, Data Analysis, Risk Management, Documentation

Experience
1+ yrs