Wholesale Quantitative Research Associate

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Commercial & Investment Bank

Develop and enhance quantitative models for wholesale credit risk measurement, credit loss estimation, and stress testing. Implement numerical methods and high-performance computing solutions, analyze large datasets, and apply machine learning techniques within a financial services context. The role requires collaboration with business, finance, and technology teams, and communication with senior stakeholders and regulators.

What you'd actually do

  1. Develop wholesale credit risk measurement models for portfolios such as Commercial and Industrial loans and structured product vehicles
  2. Build credit loss models supporting the bank’s Current Expected Credit Loss estimation
  3. Develop stress testing models supporting Comprehensive Capital Analysis and Review processes
  4. Create scorecard and modeling approaches to measure credit risk for commercial and corporate clients
  5. Assess model performance, limitations, and use appropriateness to identify and monitor model risk

Skills

Required

  • Master’s degree or higher in a quantitative discipline
  • 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 have

  • Doctorate in a quantitative discipline
  • 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

What the JD emphasized

  • quantitative models
  • model methodology
  • model governance
  • model risk
  • model estimation
  • model lifecycle tooling

Other signals

  • Develop quantitative models
  • Build credit loss models
  • Develop stress testing models
  • Create scorecard and modeling approaches
  • Assess model performance
  • Design efficient numerical methods
  • Implement high-performance computing solutions
  • Analyze large, real-world datasets
  • Machine learning techniques