Director - Market Risk Quant

A leading American Investment Bank is seeking to hire a Director within its Market Risk Quantitative Analytics team in Chicago. This individual will play a key role in the development, enhancement, and implementation of market risk and stress testing models supporting regulatory capital, risk measurement, and quantitative analytics across a complex global markets platform. The position offers significant exposure to senior stakeholders across Risk, Capital, Technology, Model Risk Management, and Front Office teams while contributing to critical regulatory and strategic risk initiatives.

The team is responsible for the design, implementation, and ongoing performance assessment of market risk and stress testing models used for both regulatory and internal risk management purposes. This includes model development, scenario design, backtesting, benchmarking, model monitoring, statistical analysis, regulatory submissions, and remediation efforts related to validation, audit, and regulatory findings. The successful candidate will help drive model enhancements while ensuring methodologies remain robust, transparent, and aligned with evolving regulatory expectations, including Basel and FRTB requirements.

This opportunity is ideal for a quantitative professional with a strong background in market risk modeling, stress testing, derivatives pricing, and regulatory capital frameworks. Candidates should possess advanced programming skills, deep knowledge of risk measurement methodologies, and the ability to lead complex quantitative initiatives while partnering effectively with Risk, Capital, Technology, and Front Office stakeholders. Prior experience with VaR, Stressed VaR, FRTB, CCAR, Expected Shortfall, and model governance processes is highly desirable.

• Lead the development, implementation, and enhancement of market risk and stress testing models supporting regulatory capital calculations and enterprise risk management.
• Perform quantitative analysis for model development, scenario design, benchmarking, backtesting, and ongoing model performance monitoring.
• Develop and enhance methodologies supporting VaR, Stressed VaR, Risks Not in VaR (RNiV), Expected Shortfall, FRTB, and other regulatory market risk frameworks.
• Design, implement, and analyze market risk stress testing scenarios, including result interpretation, reporting, and identification of key risk drivers.
• Partner closely with Risk Management, Capital Management, Front Office, Technology, and Model Validation teams to support model implementation and governance initiatives.
• Prepare technical documentation, regulatory submissions, and supporting materials for model reviews, audits, and regulatory examinations.
• Identify model weaknesses, recommend enhancements, and support remediation efforts through statistical analysis, benchmarking, and performance testing.

• Master's or PhD in Financial Engineering, Mathematics, Statistics, Physics, Computer Science, Economics, or a related quantitative discipline.
• 7+ years of experience in Market Risk Modeling, Quantitative Research, Quantitative Finance, Model Development, or a related quantitative function.
• Strong understanding of Value-at-Risk (VaR), Stressed VaR, Expected Shortfall, stress testing methodologies, and regulatory capital frameworks.
• Experience developing or reviewing models related to market risk, FRTB, CCAR, regulatory capital, derivatives valuation, scenario analysis, or stress testing.
• Advanced programming experience in Python, with exposure to additional programming languages or quantitative technologies considered a plus.
• Solid understanding of derivatives pricing and financial mathematics across fixed income, rates, and other traded products.
• Excellent written and verbal communication skills with the ability to present complex quantitative findings to senior management, validators, auditors, and regulators.
• Experience supporting regulatory examinations, model governance initiatives, or large-scale quantitative model implementation projects is highly desirables