Lead Associate Principal, Quantitative Risk Management

About the position

The Lead Associate Principal is responsible for one or more functions within Quantitative Risk Management (QRM) to develop and maintain risk models for margin, clearing fund and stress testing: model analytics and performance monitoring; model prototyping and testing; and model implementation. The Lead Associate Principal will collaborate with other quantitative analysts, business users, data & technology staff, and model validation colleagues to implement new models and enhance existing models.

Responsibilities

• Develop models for pricing, margin risking and stress testing of financial products and derivatives.
• Design, implement and maintain model prototypes, model library and model testing tools using best industry practices and innovations.
• Implement new models into model library and enhance existing models.
• Write and review documentations (whitepapers) for the models, model prototypes and model implementation.
• Perform model performance testing, including portfolio back-testing using historical data.
• Review implementation of models and algorithms focusing on requirement verification, coding, and testing quality.
• Conduct comprehensive quality assurance testing on model library including constructions of test cases, automation of model unit testing and creations of reference models if needed.
• Participate in model code reviews, model release testing (including margin impact analysis and baseline support and troubleshooting during model library integration with production applications) and production support.
• Support the launch of new products.
• Provide quantitative analysis and support to risk managers on pricing, margin, and risk calculations.
• Communicate model analysis to professionals across OCC and collaborate with cross-functional departments.

Requirements

• Strong quantitative skills, ability to demonstrate deep understanding in financial mathematics (derivatives pricing models, stochastic calculus, statistics and probability theory, advanced linear algebra).
• Econometrics, data analysis (e.g., time series analysis, GARCH, fat-tailed distributions, copula, etc.) and machine learning techniques.
• Numerical methods and optimization: e.g. Monte Carlo simulation and finite difference techniques.
• Risk management methods (value-at-risk, expected shortfall, stress testing, backtesting, scenario analysis).
• Good understanding of markets and financial derivatives in equities, interest rate, and commodity products.
• Model development and prototyping requires development skills.
• Strong problem-solving skills: Be able to accurately identify a problem's source, severity, and impact to determine possible solutions and needed resources.
• Ability to challenge model methodologies, model assumptions, and validation approach.
• Proficiency in technical and scientific documentation (e.g., white papers, user guides, etc.).

Nice-to-haves

• Proficiency in database technology and query languages (such as SQL). Non-relational DB and other Big Data, cloud-based computing experience is a plus.
• Experience in a scripting language such as Python, R or MATLAB.
• Experience in office technology such as PowerPoint, Confluence, Word, and Excel.

Benefits

• A hybrid work environment, up to 2 days per week of remote work.
• Tuition Reimbursement to support your continued education.
• Student Loan Repayment Assistance.
• Technology Stipend allowing you to use the device of your choice to connect to our network while working remotely.
• Generous PTO and Parental leave.
• 401k Employer Match.
• Competitive health benefits including medical, dental and vision.