Risk Model Development- Intermediate Analyst

As a member of Citis Risk Modeling Solutions department, you will play a crucial role in developing benchmark risk models for Citi's secured portfolios, specifically focusing on CCAR, CECL, climate risk, and other regulatory/internal purposes. Your responsibilities will include: - Participating in the development of champion/benchmark models for CCAR, CECL, and other regulatory/internal purposes for Citi's secured portfolios. - Independently conducting data cleansing and analysis to identify static and dynamic portfolio drivers and macroeconomic drivers for portfolio risk performances. - Building PD/EAD/LGD models and performing statistical analysis and backtests. - Conducting forecast sensitivity analysis and model robustness tests. - Providing model implementation and validation support with minimal manager supervision. - Creating and reviewing Model Development Documents for validation and supporting Annual Model Reviews and Ongoing Performance Assessment. - Collaborating with cross-functional teams, including business stakeholders, model validation and governance teams, and model implementation team. - Preparing responses/presentations to regulatory agencies on all CCAR/CECL/IFRS9/Climate models built. - Effectively communicating model results to technical and non-technical senior audiences. Qualifications: - 5+ years of experience in quantitative analysis, statistical modeling, loss forecasting, loan loss reserve modeling, or econometric modeling. - In-depth knowledge of using statistical models to solve business problems. - Experience in end-to-end credit risk modeling, CCAR, and CECL is preferred. - Strong programming skills in SAS, SQL, Python, R, and quantitative analytics. - Excellent communication skills to translate model design, specification, and performance details. Education: - Masters/University degree in Economics, Mathematics, Statistics, Finance, or other quantitative discipline. - PhD degree in Statistics, Economics, Finance, Biomedical Engineering, or other quantitative discipline is preferred. In addition to the technical skills required for the role, you should possess good communication skills to effectively convey complex technical information to various audiences. Your ability to work collaboratively with different teams and your understanding of regulatory requirements will be essential in ensuring the timely completion of projects with high quality. As a member of Citis Risk Modeling Solutions department, you will play a crucial role in developing benchmark risk models for Citi's secured portfolios, specifically focusing on CCAR, CECL, climate risk, and other regulatory/internal purposes. Your responsibilities will include: - Participating in the development of champion/benchmark models for CCAR, CECL, and other regulatory/internal purposes for Citi's secured portfolios. - Independently conducting data cleansing and analysis to identify static and dynamic portfolio drivers and macroeconomic drivers for portfolio risk performances. - Building PD/EAD/LGD models and performing statistical analysis and backtests. - Conducting forecast sensitivity analysis and model robustness tests. - Providing model implementation and validation support with minimal manager supervision. - Creating and reviewing Model Development Documents for validation and supporting Annual Model Reviews and Ongoing Performance Assessment. - Collaborating with cross-functional teams, including business stakeholders, model validation and governance teams, and model implementation team. - Preparing responses/presentations to regulatory agencies on all CCAR/CECL/IFRS9/Climate models built. - Effectively communicating model results to technical and non-technical senior audiences. Qualifications: - 5+ years of experience in quantitative analysis, statistical modeling, loss forecasting, loan loss reserve modeling, or econometric modeling. - In-depth knowledge of using statistical models to solve business problems. - Experience in end-to-end credit risk modeling, CCAR, and CECL is preferred. - Strong programming skills in SAS, SQL, Python, R, and quantitative analytics. - Excellent communication skills to translate model design, specification, and performance details. Education: - Masters/University degree in Economics, Mathematics, Statistics, Finance, or other quantitative discipline. - PhD degree in Statistics, Economics, Finance, Biomedical Engineering, or other quantitative discipline is preferred. In addition to the technical skills required for the role, you should possess good communication skills to effectively convey complex technical information to various audiences. Your ability to work collaboratively with different teams and your understanding of regulatory requirements will be essential in ensuring the timely completion of projects with high quality.