Corporate and Investment Banking - Wholesale Credit Risk - Associate

JPMorgan Chase & Co.•Jersey City, NJ

6d•$135,000 - $150,000

About The Position

As a Quantitative Researcher in Wholesale Credit Risk Modeling, you develop and enhance quantitative models that support responsible growth and strong risk controls. You will partner with credit risk, finance, and technology teams to translate business needs into scalable model solutions. You will help us strengthen model methodology, documentation, and governance for key regulatory and risk management use cases. You will present model approaches, results, and limitations to senior stakeholders and model governance forums.

Requirements

Master’s degree in a quantitative or computing discipline (computer science, engineering, data science, statistics etc.) or a bachelor’s degree and least 3 years of relevant experience.
Hands-on programming experience in Python for data analysis and modeling (including pandas and NumPy)

Nice To Haves

Demonstrated experience working with large datasets and building repeatable data pipelines for modeling.
Experience designing or implementing AI/ML workflows, agentic systems, or intelligent automation in a risk, analytics, or data-intensive environment.
Ability to explain complex technical concepts to non-technical stakeholders in clear, concise language, including limitations and control considerations.

Responsibilities

Develop software and analytic solutions to enable statistical model developers and users build, improve, test, maintain, use and monitor models.
Partner with quantitative model developers to identify bottlenecks in model development, validation, and documentation, then implement AI-enabled solutions that are fit-for-purpose under model risk management expectations.
Design and build agentic AI workflows tooling that accelerates model calibration, back-testing, sensitivity analysis, performance monitoring, and diagnostics while preserving methodological integrity and transparency.
Develop reusable, well-tested Python frameworks and libraries that enable broader adoption across the modeling organization.
Communicate workflow designs, efficiency gains, and limitations clearly to model governance committees, senior leadership, and cross-functional partners