Vice President, Counterparty Credit Risk Modeling

Role Objectives

• Lead the development, enhancement, and ongoing maintenance of counterparty credit risk (CCR) models across multiple asset classes, with a focus on quantitative rigor, model robustness, and regulatory compliance.
• Enhance Potential Future Exposure (PFE) modeling frameworks by evaluating and comparing alternative modeling methodologies, including Monte Carlo–based approaches, stress testing methodologies, and margin-related exposure models.
• Drive the analysis and modeling of new and complex derivative products within vendor platforms, leading technical discussions on model design, assumptions, and implementation feasibility.
• Collaborate closely with Risk IT and quantitative infrastructure teams to implement new CCR models, enhance existing model components, and ensure accurate integration into production systems. Design, develop, and maintain a broad suite of stress testing scenarios, including historical, hypothetical, and forward‑looking scenarios, to assess counterparty exposure under adverse market conditions.
• Ensure that CCR modeling frameworks and analytical processes comply with internal model governance standards and external regulatory requirements through robust documentation, testing, and ongoing monitoring.
• Perform continuous counterparty surveillance and exposure monitoring, conducting detailed ad‑hoc quantitative analyses to support 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.

Qualifications and Skills

• Master’s degree in mathematics, statistics, or a related field and 8 years of experience.
• Experience must include the following: counterparty credit risk modeling, including Potential Future Exposure (PFE), margin-related exposure measures, and simulation-based exposure profiles across asset classes; Monte Carlo simulation, stochastic processes, and numerical methods for modeling risk factors and exposure distributions;  design, implementation, and analysis of comprehensive stress testing scenarios, including historical, hypothetical, and forward-looking scenarios; regulatory standards governing counterparty credit risk models, including model validation, documentation, testing, and ongoing performance monitoring requirements; Python for quantitative modeling, model testing, data analysis, and implementation support; statistical testing, backtesting methodologies, and model performance assessment, including regression, goodness-of-fit, and stability tests; derivative products across asset classes, including the ability to analyze exposure profiles for new and complex products using vendor modeling platforms; collaboration 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.

The required skills do not need to be maintained over the full term of the required experience and may be gained through employment or graduate studies.

The role is eligible for an annual discretionary incentive award. In addition to cash compensation, SMBC offers a competitive portfolio of benefits to its employees.

For benefit information, please see the Careers page