Vice President, Exposure Management & Risk Analytics

What you'll do

• Build and maintain stress testing, margin analytics, and risk modeling solutions using Python and SQL.
• Manage the production delivery, monitoring, and incident management of analytics infrastructure.
• Enforce production controls through version management, code reviews, data validation, and documentation.
• Execute quantitative modeling initiatives to translate market and risk insights into scalable analytics.
• Enhance portfolio stress testing frameworks and perform scenario analyses for market and credit risks.
• Develop margin requirement analytics covering concentration, liquidity, volatility, and derivatives-related risks.
• Ensure analytical models align with regulatory requirements and maintain necessary governance documentation.
• Implement automation solutions to improve risk analysis, operational efficiency, and reporting capabilities.

What we're looking for

• Minimum 5 years of experience in financial services, risk management, quantitative analysis, or a related technical field.
• Experience developing, deploying, and supporting analytics solutions within a production environment.
• Advanced proficiency in Python and SQL for data analysis, modeling, and automation.
• Knowledge of market risk concepts, including volatility, concentration risk, derivatives risk, and stress testing methodologies.
• Knowledge of US Wealth Management products and related risk management practices.
• Familiarity with Regulation T and Portfolio Margin frameworks.
• Experience implementing controls related to code development, testing, validation, and documentation.
• Series 7 license or the ability to obtain the license within 120 days.
• Experience developing or enhancing risk analytics methodologies and frameworks (preferred).
• Knowledge of financial mathematics, derivatives, options, or quantitative risk management techniques (preferred).
• Experience supporting stress testing, scenario analysis, or portfolio risk assessment programs (preferred).
• Familiarity with data science, machine learning, or advanced analytics techniques (preferred).
• Experience preparing methodology, validation, or model governance documentation (preferred).
• Knowledge of margin lending products and securities-based lending risk management practices (preferred).