Analyst - Wholesale Credit Risk Quantitative Research

About the role

Design and develop wholesale credit loss forecasting models for stress testing, capital planning, risk appetite frameworks, and loan loss reserves, using statistical and econometric methods and machine learning where appropriate. Conduct model back-testing and monitoring, work with Data and Technology teams on reliable data pipelines, and document and communicate model results for internal and regulatory reviews. A degree in a quantitative field and strong foundations in calculus, linear algebra, probability, and statistics are required, along with knowledge of statistical modeling and experience handling large panel datasets. Candidates should be proficient in Python or C++ and an object-oriented programming language, follow sound software practices, and communicate effectively; two or more years of relevant experience and credit-risk or financial-product knowledge are preferred.