below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$126,100–$184,825.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1239 open roles
on FindRole.
Listed pay typically runs
$136,000–$207,500
across 6 roles with salary data.
As a Wholesale Credit Quantitative Research - Senior Associate, you will join the Risk Management and Compliance team to develop and maintain credit risk models for firmwide stress testing and loss forecasting frameworks, including CCAR, CECL, and IFRS 9. You will perform credit risk modeling at single-obligor and portfolio levels, focusing on probability of default, loss given default, and exposure at default. Daily tasks involve analyzing large financial, credit, and macroeconomic datasets, performing data cleaning, and constructing features. You will utilize Python and statistical methods like GLMs, logistic regression, time-series forecasting, clustering, and decision trees. Additionally, you will apply interpretable AI/ML methods, maintain model libraries, and produce technical documentation for governance and regulatory review while partnering with technology and data stakeholders to deliver production-ready solutions.
Perform credit risk modeling at both the single-obligor and portfolio levels.
Support the research, development, and enhancement of PD forecasting and rating migration models.
Analyze large financial, credit, and macroeconomic datasets including data cleaning and feature construction.
Perform model testing, monitoring, diagnostics, and validation support analyses.
Partner with technology teams to maintain model libraries following software development standards.
Prepare technical documentation to support internal governance and regulatory review requests.
Apply interpretable AI/ML methods while maintaining strong controls and explainability.
What we're looking for
Master’s or Ph.D. in a quantitative discipline such as Statistics, Econometrics, Economics, Math, Engineering, Computer Science, Finance, or Physics.
Strong foundation in statistical and econometric modeling, including GLMs, logistic regression, time-series forecasting, clustering, and decision trees.
Experience working with large datasets, specifically panel and time-series data, with strong data analysis skills.
Hands-on Python programming experience.
Strong problem-solving, communication, and the ability to manage multiple workstreams.
Experience producing technical documentation for governance and review.
Exposure to wholesale credit risk, CCAR, CECL, IFRS 9, numerical methods, or AI/ML in risk contexts (preferred).
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