Risk Management - Wholesale Quantitative Research, Associate

J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 

Bring your Expertise toJPMorganChase. As part of Risk Management and Compliance, you are at the center of keepingJPMorganChasestrong and resilient. You help the firm grow its business in a responsible way byanticipatingnew andemergingrisks andusing your expert judgement to solve real-world challenges thatimpactour company,customersand communities. Our culture in Risk Management and Compliance is all aboutthinking outside the box, challenging thestatus quo,and striving to be best-in-class.

As a Quantitative Researcher in Wholesale Credit Risk Modeling team, , you will 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 modelmethodology, 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.

Job Responsibilities

• Develop wholesale credit risk measurement models for portfolios such as Commercial Real Estateloans and structured product vehicles
• Build credit lossmodels supportingthe bank’sCurrent Expected Credit Lossestimation
• Develop stress testing models supporting Comprehensive Capital Analysis and Review processes
• Create scorecard and modeling approaches to measure credit risk for Commercial Real Estate clients
• Assess model performance, limitations, and use appropriateness toidentifyandmonitormodel risk
• Design efficient numerical methods to support model estimation, calibration, and validation
• Implement high-performance computing solutions to improve model runtime and scalability
• Build reusable analytics software frameworks and integrate model outputs into downstream systems
• Analyze large, real-world datasets to derive insights that improve model accuracy and stability
• Partner with credit officers, portfolio managers, finance, and technology to deliver business-ready solutions
• Communicatemethodology, results, and limitations clearly to model governance committees and regulators

Required Qualifications, Capabilities, and Skills

• Master’s degree or higher in a quantitative discipline (for example: mathematics, physics, statistics, economics, finance, or computer science)
• 3 years of experience developing statistical and/or economic models in a financial services or risk context
• 3 years of experience applying regression and multivariate statistical techniques to real-world datasets
• 3 years of hands-on programming experience in Python for data analysis and modeling (including pandas and NumPy)
• 2 years of experience working with machine learning techniques in model development or analytics workflows
• Demonstrated experience working with large datasets and building repeatable data pipelines for modeling
• Knowledge of core banking risks and how risk is measured and managed in a wholesale credit context
• Ability to explain complex quantitative concepts to non-technical stakeholders in clear, concise language
• Proven ability to collaborate across functions and translate business needs into quantitative solutions
• Strong attention to detail, with a disciplined approach to testing, documentation, and controls
• Ability to adapt quickly, learn new domains, and deliver in a fast-paced environment

Preferred Qualifications, Capabilities, and Skills

• Doctorate in a quantitative discipline (for example: mathematics, physics, statistics, economics or finance)
• Experience developing wholesale credit risk models for Basel, Comprehensive Capital Analysis and Review, or Current Expected Credit Losses exercises
• Experience designing numerical algorithms (for example: optimization or root-finding) for model calibration
• Experience with Linux or Unix environments for research and production workflows
• Familiarity with cloud platforms and model lifecycle tooling (for example: AWS, Azure,MLflow, Kubeflow, or SageMaker)
• Experience using modern artificial intelligence tools to accelerate model development, testing, or documentation workflows
• Knowledge of graph or network analytics for counterparty or contagion risk modeling