Quant Risk Manager

Role & Responsibilities

• Lead Development: Drive the creation of advanced risk methodologies and quantitative analyses to model and quantify financial risk exposures associated with exchange-traded derivatives at a regulated exchange.
• Model Implementation: Establish and maintain financial risk models that facilitate the measurement and ongoing management of market risk, credit risk, collateral risk, and liquidity risk, in line with regulatory standards.
• Quantitative Analysis: Generate quantitative analyses to support primary inputs and risk parameters, including data aggregation, risk analysis, and stress testing, essential for the implementation and management of risk models.
• Product Development Collaboration: Engage in product development initiatives, working closely with Product Management and Risk Engineering teams to integrate robust risk management practices into the product lifecycle.
• Risk Assessment Support: Assist in internal and external risk assessments to validate risk model performance and ensure compliance with internal policies and regulatory standards.
• Stakeholder Engagement: Facilitate internal and external discussions with management, regulators, and members regarding risk models and potential enhancements to align with best practices and emerging trends.
• Industry Best Practices: Evaluate current and evolving industry and regulatory best practices to ensure risk methodologies remain effective and relevant.
• Risk Management Culture: Foster a strong culture of risk management within the organization.

Experience & Qualifications

• Educational Background: Advanced degree in a quantitative field such as quantitative finance, mathematics, computer science, or a related discipline.
• Risk Modeling Expertise: In-depth understanding of financial risk modeling for derivatives (futures, options), including stress testing and portfolio analysis to evaluate financial exposure.
• Methodology Development: Proven experience in developing risk approaches and methodologies specific to derivatives risk management, particularly within clearing and/or prime brokerage contexts.
• Statistical Proficiency: Strong skills in statistical modeling and data analysis techniques, including time series analysis, regression analysis, machine learning, and optimization methods.
• Adaptability: Ability to thrive in a dynamic environment where priorities may shift frequently.
• Communication Skills: Excellent communication abilities, capable of effectively presenting complex concepts and findings to both technical and non-technical audiences.