As a Quantitative Researcher in Wholesale Credit Risk Modeling team, you will develop and enhance quantitative models that support responsible growth and strong risk controls. You will partner with credit risk, finance, and technology teams to translate business needs into scalable model solutions. You will help us strengthen model methodology, documentation, and governance for key regulatory and risk management use cases. You will present model approaches, results, and limitations to senior stakeholders and model governance forums.

Job responsibilities:

• Develop wholesale credit risk measurement models for portfolios such as Commercial and Industrial loans and structured product vehicles, including models supporting the bank’s Current Expected Credit Loss estimation and Comprehensive Capital Analysis and Review processes
• Create rating and credit officer-facing modeling approaches to measure credit risk for commercial and corporate clients
• Assess model performance, limitations, and use appropriateness to identify and monitor model risk; design efficient numerical methods to support model estimation, calibration, and validation
• Implement high-performance computing solutions to improve model runtime and scalability
• Build reusable analytics software frameworks and integrate model outputs into downstream systems
• Analyze large, real-world datasets to derive insights that improve model accuracy and stability
• Partner with credit officers, portfolio managers, finance, and technology to deliver business-ready solutions and communicate methodology, results, and limitations clearly to model governance committees and regulators

Required qualifications, capabilities, and skills:

• Master’s degree or PhD in a quantitative discipline (for example: economics, finance, physics, mathematics, or computer science)
• 5 plus years of experience developing statistical and/or economic models in a financial services or risk context, including hands-on programming experience in Python for data analysis and modeling (including pandas and NumPy)
• 3 plus years of experience working with machine learning techniques in model development or analytics workflows, with demonstrated experience working with large datasets and building repeatable data pipelines for modeling
• Experience using modern artificial intelligence tools to accelerate model development, testing, or documentation workflows
• Knowledge of core banking risks and how risk is measured and managed in a wholesale credit context
• Ability to explain complex quantitative concepts to non-technical stakeholders in clear, concise language; a proven ability to collaborate across functions and translate business needs into quantitative solutions
• Strong attention to detail, with a disciplined approach to testing, documentation, and controls
• Ability to adapt quickly, learn new domains, and deliver in a fast-paced environment

Preferred qualifications, capabilities, and skills:

• Doctorate in a quantitative discipline (for example: economics, finance, mathematics, or physics)
• Experience designing numerical algorithms (for example: optimization or root-finding) for model calibration; knowledge of graph or network analytics for counterparty or contagion risk modeling
• Experience with Linux or Unix environments for research and production workflows; familiarity with cloud platforms and model lifecycle tooling (for example: AWS, Azure, MLflow, Kubeflow, or SageMaker)