Lead, Quantitative Risk Development

Job Description

**Lead, Quantitative Risk Development**

Salary: Open + Bonus

Location: Chicago, IL

Hybrid: 3 days onsite, 2 days remote

*This role is open to H1B transfer*

**Qualifications**

– Master’s degree in quantitative fields such as computer science, mathematics, physics, finance/financial engineering
– 7+ years of experience in quantitative areas in finance and/or development experience in model implementation and testing
– For model development and prototyping role: proficiency in a scripting language such as Python, R or MATLAB
– Experience with automated testing frameworks (e.g., Junit, TestNG, PyTest, etc.)
– 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; Monte Carlo simulation and finite difference techniques
– Risk management methods (value-at-risk, expected shortfall, stress testing, back testing, scenario analysis)
– Financial products knowledge: good understanding of markets and financial derivatives in equities, interest rate, and commodity products
– Strong programing skills. Able to read and/or write code using a programming language (e.g. Java, C++, Python, R, Scala, etc.) in a collaborative software development setting: Model development and prototyping requires advanced development skills in Python and database manipulation
– 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.)

**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 documentation (white papers) 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
– Provide quantitative analysis and support to risk managers on pricing, margin, and risk calculations