As a Quantitative Research Wholesale Credit Risk Modeling Analyst within the Wholesale Credit team, you will design, analyze, and deliver quantitative models to support the firm’s Wholesale Credit Stress (CCAR, ICAAP, Risk Appetite) and loan loss reserves models. You will use statistical techniques & tools for building forecasting models. This role will provide you with the opportunity to work with other experienced Wholesale Credit Quantitative Researchers and business partners, enhancing your quantitative as well as business skills.

Job Responsibilities

Work as a quantitative researcher to design and develop PPNR forecasting models for regulatory purposes. 

Minimum Skills, Experience and Qualifications

If you meet the minimum requirements below, you are encouraged to apply to be considered for this role.

You have a degree in Engineering, Financial Engineering, Computer Science, Mathematics, Sciences, Statistics, Econometrics, or other quantitative fields You have a strong background in the following topics – Calculus, Linear Algebra, Probability, and Statistics You have solid theoretical and practical knowledge of statistical methods and models: generalized linear models, time-series analysis, clustering, decision trees, logistic regression.  You are experienced in handling large amount of panel data, and data cleaning/filtering.  You demonstrate proficiency in at least one of the object-oriented programming languages, and are good at one of Python or R Ability to solve problems creatively while working in a dynamic environment. Eagerness to learn about Credit Risk, Risk Parameters, Regulatory and Accounting concepts 1-3 years of relevant experience would be preferred.

Additional Skills, Experience and Qualifications

The following additional items would be a plus but not a mandatory requirement.

Knowledge of Wholesale Credit products and experience in development of loss/PPNR forecasting models for regulatory exercises  Knowledge of different types of financial products and asset classes, options pricing theory, financial regulations, machine learning , or high-performance computing would be a plus Proven ability to develop collaborative relationships with key internal partners to achieve objectives and prioritizations

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.