Sr. Data Scientist-Risk Credit Modeling (Hybrid)

The Assistant Vice President role of global secured portfolio model development reports to a manager of risk analytics and modeling within the Risk Model Utility (RMU) of Personal Banking and Wealth Management (PBWM).  This individual will build champion/benchmark risk models such as PD, EAD and LGD models for Citi's international and U.S. secured portfolios for CCAR, CECL, ICAAP, IFRS9, climate risk stress test and other regulatory usage. 

Responsibilities:

Position responsibilities include but not limited to the following activities:

• Participate in building champion/benchmark models for CCAR, CECL, IFRS9, climate risk stress test and other regulatory purposes for Citi's international and U.S. secured portfolios.
• Under manager's guidance, perform data cleansing and analysis, identify static and dynamic portfolio drivers and macroeconomic drivers for portfolio risk performances, build PD/EAD/LGD models and conducting statistical analysis and backtests, perform forecast sensitivity analysis and model robustness tests, and provide model implementation and validation support.
• Create Model Development Document for validation and supporting Annual Model Reviews and Ongoing Performance Assessment of implemented models.
• Participate in model revalidation, model change and related documentation and validation support efforts.
• Ensure timely completion of assigned projects with high quality.
• Work closely with cross functional teams, including country/region’s business stakeholders, model validation and governance teams, and model implementation team
• Prepare responses/presentations to regulatory agencies on all CCAR/CECL/IFRS9/Climate models built

Qualifications:

• 5+ years of experience in performing quantitative analysis, statistical modeling, loss forecasting, loan loss reserve modeling, or econometric modeling and in-depth knowledge on the use of statistical models to solve business problems (years of experience in Master or PhD programs of Statistics, Economics, Finance, Biomedical Engineering or other highly quantitative discipline counts).
• Past experience of climate risk stress testing or catastrophe modeling preferred
• Experience of end-to-end credit risk modeling highly preferred
• Experience of CCAR and CECL highly preferred
• Strong programming (SAS, Python, R, etc.) and quantitative analytics (regression, time series, decision tree, linear/nonlinear optimization etc.) skills preferred
• Strong communication skills required to translate model design, specification and performance details to technical and non-technical audiences.

Education:

• Bachelor’s/University degree or equivalent experience
• PhD degree in Statistics, Economics, Finance, Biomedical Engineering or other quantitative discipline preferred

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