Wholesale Credit Quantitative Research - Senior Associate

JPMorgan Chase; Plano (TX); On-site; USD 110,000 - 170,000; Full time

Job summary

JPMorgan Chase is seeking an Associate in Wholesale Credit Risk Quantitative Research to develop and maintain credit risk models used in stress testing and loss forecasting, including CCAR, CECL, and IFRS 9. You will work with large datasets and collaborate across governance, technology, data, and business teams to deliver production-ready solutions.

The role emphasizes quantitative modeling, documentation, and the application of interpretable AI/ML methods with strong controls.

Qualifications

• Master's or Ph.D. in a quantitative discipline.
• 1+ years of relevant experience.
• Strong foundation in statistical/econometric modeling (GLMs, logistic regression, time-series).
• Experience working with large datasets and data analysis skills.
• Hands-on Python programming.
• Strong problem-solving, communication, and ability to manage multiple workstreams.
• Experience producing technical documentation (including governance/review).

Responsibilities

• Perform basic credit risk modeling at single-obligor and portfolio levels.
• Support research, development, and enhancement of PD forecasting and rating migration models.
• Analyze large financial/credit/macro datasets; data cleaning and feature construction.
• Perform model testing, monitoring, diagnostics, and validation support analyses.
• Partner with technology teams to maintain model libraries per standards.
• Prepare clear technical documentation for governance and regulatory review requests.
• Apply interpretable AI/ML methods with strong controls and explainability.

Skills

Statistical modeling

Python programming

Data analysis

Communication

Education

MS/PhD in quantitative field

Job description

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