As part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks and using your expert judgment to solve real-world challenges that impact our company, customers, and communities.

The Portfolio Analytics Grading team focuses on the overall risk grading framework for Wholesale Credit Risk clients, which includes corporations across a diverse range of industries. The team builds new models and methodologies, develops expertise in risk grading clients across various industries, and leverages portfolio data analysis to enhance credit risk assessments.

You'll support the development of credit risk grading models and frameworks for the Wholesale Credit Risk organization. This means hands-on quantitative work, exposure to model methodology, and the opportunity to build foundational expertise in credit risk analysis.

Job responsibilities:

• Support the development and enhancement of risk grading frameworks, contributing to methodology design, data preparation, implementation, and validation under the guidance of senior team members
• Build and maintain Python-based pipelines for managing large credit datasets, running back tests, and producing portfolio-level analytics that stress test model assumptions
• Conduct analyses on portfolio segments — helping to identify concentration risks, migration trends, and grading inconsistencies that inform framework calibration
• Assist in applying LLMs to synthesize unstructured data (financials, research, news) into structured datasets for use in model development
• Help translate analytical findings into clear narratives for senior committees and regulators, contributing to decks, memos, and committee materials
• Collaborate with Technology, Model Risk, and Controls teams to support the building of tools and workflows that operationalize new grading methodologies

Required qualifications, capabilities, and skills:

• Bachelor's degree in a quantitative field such as Finance, Economics, Mathematics, Statistics, or a related discipline
• Up to 2 years of relevant experience in financial analytics, credit risk, or a related quantitative field (internship experience considered)
• Proficiency in Python with experience manipulating datasets (pandas, SQL, Num Py) and building reproducible analytical workflows
• Foundational understanding of quantitative modeling concepts such as back testing, sensitivity analysis, and scenario modeling
• Strong written and verbal communication skills with the ability to present analytical work clearly
• Familiarity with or willingness to learn core credit risk concepts such as PD and LGD