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Discover your future at Citi

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Job Overview

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.  

Responsibilities:

Position responsibilities include but not limited to the following activities:

• 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.

Qualifications:

• 2+ years of experience

• 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).

• 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.

Skillset:

• Quantitative Analysis

• Statistical Modeling

• Loss forecasting/Loan Loss Reserve Modeling/Econometric Modeling

• Decision Modeling

• Model Monitoring and Business Analytics ​

• Climate Risk Stress Testing or Catastrophe Modeling

• Credit Risk Modeling

• CCAR/CECL Regulations

• SAS, SQL, Python, R

Education:

• 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

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Job Family Group:

Risk Management

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Job Family:

Model Development and Analytics

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Time Type:

Full time

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Most Relevant Skills

Analytical Thinking, Business Acumen, Constructive Debate, Data Analysis, Escalation Management, Policy and Procedure, Policy and Regulation, Risk Controls and Monitors, Risk Identification and Assessment, Statistics.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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