Analyst - Wholesale Credit Risk Quantitative Research

Job Summary

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