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Citi’s Risk Modeling Solutions department is responsible for the development, delivery, and monitoring of all credit risk models across Citi’s consumer lending portfolios globally. These models span two core activities; granting and managing credit to individual customers and delivering loss forecasts for stress testing (ex. CCAR), loan loss reserving (ex. CECL), and business planning.
Job Responsibility:
Participate in building champion/benchmark models for CCAR, CECL, IFRS9, climate risk, and other regulatory/internal 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
Understand model variables and economic forecasts and conduct drill down analysis and reporting of model performances
Deliver comprehensive write-up of ongoing model performance assessment, Annual Model Review / Revalidation documents
Requirements:
2+ years of experience
Experience in performing quantitative analysis, statistical modeling, loss forecasting, loan loss reserve modeling, or econometric modeling
In-depth knowledge on the use of statistical models to solve business problems
Experience of end-to-end credit risk modeling highly preferred
Experience of CCAR and CECL preferred
Understanding of traditional modeling processes (linear/ logistic regression, segmentation, decision tree) and machine learning algorithms (Random Forest, Gradient Boosting, XG Boost, SVM, etc.), time series, linear/nonlinear optimization
Experience in reporting (risk/marketing) in portfolio reporting, model monitoring, generating business insights
Strong programming (SAS, SQL, 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
Master’s/University degree or equivalent experience in Economics, Mathematics, Statistics, Finance of other quantitative discipline
PhD degree in Statistics, Economics, Finance, Biomedical Engineering or other quantitative discipline preferred