Bring your expertise to JPMorganChase. As part of Risk Management and Compliance, you’ll help keep the firm strong and resilient by anticipating emerging risks and applying sound judgment to solve real-world challenges for our company, customers, and communities. Our culture values curiosity, challenging the status quo, and striving to be best-in-class.
Job Summary
As a Quantitative Research Analyst, Wholesale Credit Risk Modeling within Risk Management and Compliance, you will design, analyze, and deliver quantitative models that support wholesale credit stress testing, internal capital planning, risk appetite frameworks, and loan loss reserves under current expected credit loss standards. You will use statistical techniques and tools to build forecasting models and run back-testing and annual monitoring in line with model governance standards. You will work with experienced quantitative researchers and business partners while building both quantitative and business skills. You will contribute to clear model documentation and communication of model results for internal and regulatory reviews.
Job Responsibilities
- Design and develop loss forecasting models for regulatory purposes.
- Conduct back-testing, performance monitoring, and annual reviews of models in line with model governance standards.
- Apply statistical and econometric techniques, and machine learning where appropriate, for forecasting, risk monitoring, and model explainability.
- Handle large-scale panel datasets, including data cleaning, filtering, and feature engineering.
- Partner with Data and Technology teams to implement models with robust data pipelines and controls.
- Prepare clear, comprehensive model documentation for internal and regulatory reviews, and support preparation of results and model impacts for senior stakeholders.
Required Qualifications, Capabilities, and Skills
- Degree in Engineering, Financial Engineering, Computer Science, Mathematics, Sciences, Statistics, Econometrics, or another quantitative field.
- Strong background in Calculus, Linear Algebra, Probability, and Statistics.
- Theoretical and practical knowledge of statistical methods and models, including generalized linear models, time-series analysis, clustering, decision trees, and logistic regression.
- Experience handling large amounts of panel data, including data cleaning and filtering.
- Proficiency in at least one object-oriented programming language and proficiency in Python or C++, with good software practices (version control, testing, reproducibility).
- Ability to solve problems creatively in a dynamic environment, with eagerness to learn about credit risk, risk parameters, and regulatory and accounting concepts.
- Strong written and verbal communication skills.
Preferred Qualifications, Capabilities, and Skills
- 2+ years of relevant experience.
- Knowledge of wholesale credit products and experience developing loss forecasting models for regulatory exercises.
- Knowledge of securitization and securitized products such as residential mortgage-backed securities (RMBS), asset-backed securities (ABS), and collateralized loan obligations (CLOs).
- Knowledge of bond and loan pricing, and knowledge of other financial products and asset classes, options pricing theory, or financial regulations.
- Experience with machine learning and generative AI.
- Familiarity with SQL, distributed data tools, and cloud-based workflows, or high-performance computing.
- Proven ability to build collaborative relationships with key internal partners to achieve objectives and set priorities.
Beyond that, we are interested in the things that make you unique: personal qualities, outside interests, and achievements beyond academia and professions that demonstrate the kind of person you are and the value you could bring to the team