Vice President, Counterparty Credit Risk Modeling

Sumitomo Mitsui Banking Corporation•Jersey City, NJ

1d•$189,000 - $195,400•Hybrid

About The Position

Lead the development, enhancement, and ongoing maintenance of counterparty credit risk (CCR) models across multiple asset classes, with an emphasis on quantitative rigor, model robustness, and regulatory compliance. The role enhances Potential Future Exposure (PFE) modeling frameworks by evaluating alternative methodologies, including Monte Carlo-based approaches, stress testing methodologies, and margin-related exposure models. The position leads analysis and modeling of new and complex derivative products within vendor platforms, including technical discussions of model design, assumptions, and implementation feasibility. It collaborates with Risk IT and quantitative infrastructure teams to implement new CCR models, improve existing components, and ensure accurate integration into production systems. The role develops and maintains historical, hypothetical, and forward-looking stress testing scenarios; supports compliance with internal model governance standards and external regulations through documentation, testing, and ongoing monitoring; and conducts counterparty surveillance, exposure monitoring, and ad-hoc quantitative analyses. It also partners with front office and risk stakeholders to evaluate trade- and portfolio-level exposures, estimate exposure for new transactions, and provide quantitative insight into counterparty risk.

Requirements

Master’s degree in mathematics, statistics, or a related field.
8 years of experience.
Experience in CCR modeling, including PFE, margin-related exposure measures, and simulation-based exposure profiles across asset classes.
Experience with Monte Carlo simulation, stochastic processes, and numerical methods for modeling risk factors and exposure distributions.
Experience designing, implementing, and analyzing comprehensive historical, hypothetical, and forward-looking stress testing scenarios.
Knowledge of regulatory standards for CCR models, including model validation, documentation, testing, and ongoing performance monitoring requirements.
Python skills for quantitative modeling, model testing, data analysis, and implementation support.
Experience with statistical testing, backtesting methodologies, and model performance assessment, including regression, goodness-of-fit, and stability tests.
Experience with derivative products across asset classes and analyzing exposure profiles for new and complex products using vendor modeling platforms.
Experience collaborating with Risk IT, quantitative infrastructure teams, and front office stakeholders to support model implementation and exposure estimation.
Foundation in applied mathematics, including stochastic differential equations and numerical partial differential equation (PDE) methods.

Responsibilities

Lead the development, enhancement, and ongoing maintenance of CCR models across multiple asset classes.
Enhance PFE modeling frameworks by evaluating alternative methods, including Monte Carlo approaches, stress testing, and margin-related exposure models.
Lead analysis and modeling of new and complex derivative products within vendor platforms, including technical discussions of model design, assumptions, and implementation feasibility.
Collaborate with Risk IT and quantitative infrastructure teams to implement CCR models, enhance model components, and integrate them accurately into production systems.
Design, develop, and maintain historical, hypothetical, and forward-looking stress testing scenarios to assess counterparty exposure under adverse market conditions.
Ensure CCR modeling frameworks and analytical processes comply with internal model governance standards and external regulatory requirements through documentation, testing, and ongoing monitoring.
Conduct continuous counterparty surveillance and exposure monitoring, as well as ad-hoc quantitative analyses supporting risk management, model performance assessment, and regulatory reviews.
Partner with front office and risk stakeholders to evaluate trade-level and portfolio-level exposures, support exposure estimation for new transactions, and provide quantitative insight into counterparty risk dynamics.