Director, Credit Stress Loss Modeling

Sumitomo Mitsui Banking Corporation•Jersey City, NJ

12d•$173,202 - $228,000•Hybrid

About The Position

SMBC Group is a global financial group headquartered in Tokyo, with more than 130 offices and 80,000 employees across nearly 40 countries. In the Americas, it provides commercial and investment banking services to corporate, institutional, and municipal clients. The Director, Credit Stress Loss Modeling develops, validates, and implements credit risk models supporting regulatory stress testing, CECL, and capital planning. The role supports credit grading model development for wholesale credit portfolios and leads end-to-end development, calibration, and documentation of PD, LGD, EAD, and loss forecasting models. It develops scenario-based stress testing frameworks, including those using CCAR scenarios, and contributes to risk scoring models for IRB approaches, pricing, underwriting, and portfolio management. The position applies advanced statistical techniques, including machine learning and survival analysis, to improve model performance and scalability. It works with Model Risk and Validation teams and model owners to resolve validation findings and meet governance standards. The role monitors model performance through back-testing, benchmarking, and sensitivity analysis; documents assumptions, limitations, and remediation plans; and supports audits and regulatory examinations. It also collaborates with Finance, Risk, and IT to operationalize models and integrate them into reporting workflows, while mentoring junior quantitative analysts and promoting knowledge sharing on topics such as AI/ML applications and climate risk integration. This role permits hybrid remote work. Employees participate in a hybrid workforce model combining work from home and an SMBC office, and must live within a reasonable commuting distance of their office. The specific hybrid schedule is discussed during the interview process.

Requirements

Master’s degree in statistics, mathematics, applied science, or a related field.
10 years of experience.
Experience using Python, SAS, R, or another programming language for quantitative analytics and modeling.
Experience with CCAR and regulatory stress testing frameworks.
Experience with vendor data sources, including Moody’s CRD/DRD, S&P data, Trepp data, and CoStar datasets.
Experience in wholesale credit model development, including commercial real estate (CRE) and/or commercial and industrial (C&I) portfolios.
Experience in statistical and econometrics modeling.
Experience in data analysis.

Responsibilities

Develop, validate, and implement credit risk models for regulatory stress testing, CECL, and capital planning.
Support credit grading model development for SMBC’s wholesale credit portfolios.
Lead end-to-end development, calibration, and documentation of PD, LGD, EAD, and loss forecasting models used in stress testing and capital planning.
Develop stress testing models to assess the impact of macroeconomic scenarios, including CCAR scenarios, and design scenario-based frameworks aligned with regulatory guidelines.
Participate in developing and calibrating risk scoring models for IRB approaches, pricing, underwriting, and portfolio management.
Enhance modeling methodologies using advanced statistical techniques, including machine learning and survival analysis.
Partner with Model Risk and Validation teams and model owners to address validation findings and comply with governance standards.
Monitor model performance through back-testing, benchmarking, and sensitivity analysis.
Document model assumptions, limitations, and remediation plans for audit and regulatory reviews.
Implement controls to ensure model accuracy and stability across economic conditions.
Support regulatory examinations and audits, ensuring adherence to SR 11-7, Basel III, and CCAR requirements.
Collaborate with Finance, Risk, and IT teams to operationalize models in production systems and integrate them into reporting workflows.
Mentor junior quantitative analysts and promote knowledge sharing on emerging topics, including AI/ML applications and climate risk integration.