Risk Management - Wholesale Quantitative Research, Associate

JPMorganChase•Jersey City, NJ

11d

About The Position

As part of Risk Management and Compliance, you will be at the center of keeping JPMorganChase strong and resilient. You will 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 culture in Risk Management and Compliance is about thinking outside the box, challenging the status quo, and striving to be best-in-class. As a Quantitative Researcher in the 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 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 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

Nice To Haves

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

Responsibilities

Develop wholesale credit risk measurement models for portfolios such as Commercial Real Estate loans and structured product vehicles
Build credit loss models supporting the bank’s Current Expected Credit Loss estimation
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 to identify and monitor model 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
Communicate methodology, results, and limitations clearly to model governance committees and regulators