Associate Principal Quantitative Risk Management Analyst

3 weeks ago


Chicago, United States Selby Jennings Full time

Position Overview:

Selby Jennings is actively partnered with the world's largest equity derivatives clearing organization to source talent for an Associate Principal Quantitative Risk Management Analyst. This position will spearhead Quantitative Risk Management (QRM) functions, focusing on developing and maintaining risk models for margin, clearing fund, and stress testing. Your responsibilities will include model analytics and performance monitoring, prototyping and testing new models, as well as overseeing their implementation to ensure robust risk management practices across various transactions including options, futures, and securities lending.

Key Responsibilities

Formulate strategies for pricing, managing risks, and stress testing various financial products and derivatives. Develop, deploy, and refine model prototypes, libraries, and testing tools adhering to industry standards and leveraging cutting-edge innovations. Draft and assess documentation (whitepapers) for models, prototypes, and implementation processes. Evaluate model performance through rigorous testing, including back-testing portfolios with historical data. Scrutinize model and algorithm implementations, emphasizing requirement validation, coding integrity, and testing efficacy. Execute comprehensive quality assurance procedures on the model library, encompassing test case construction, automation of unit testing, and creation of reference models when necessary. Participate in model code reviews, release testing, and production support activities, including margin impact analysis and troubleshooting during model library integration. Offer quantitative analysis and assistance to risk management teams regarding pricing, margin, and risk computations.

Qualifications:

Advanced understanding of financial mathematics including derivatives pricing models, stochastic calculus, and statistical concepts like probability theory and advanced linear algebra. Competence in econometrics and data analysis techniques such as time series analysis, GARCH, and copula, alongside machine learning methods. Familiarity with numerical methods, optimization, and simulation techniques like Monte Carlo simulation and finite difference methods. Knowledgeable in risk management methodologies including value-at-risk, stress testing, and scenario analysis. Sound comprehension of financial markets and derivative products spanning Capable of questioning model methodologies, assumptions, and validation strategies. Proficient in technical and scientific documentation including white papers and user guides. Familiarity with Agile/SCRUM methodologies is highly advantageous. A Master’s degree or equivalent in a quantitative field like computer science, mathematics, physics, or finance/financial engineering is required, with a preference for a PhD. Possesses five or more years of experience in quantitative finance or development, particularly in model implementation and testing.

Technical Skills

Competent in database technologies and query languages such as SQL, with experience in non-relational databases and cloud-based computing considered beneficial. Proficiency in Java (Java 8 or later) is necessary for model implementation, along with experience in scripting languages like Python, R, or MATLAB. Experience with numerical libraries, scientific computing, and automated quality assurance frameworks like Junit, TestNG, and Selenium is required for model testing. Familiarity with code repository, build, and deployment tools such as Git, GitHub, and Jenkins. Demonstrates expertise in software design, including the application of design patterns and object-oriented design principles. Experience with high-performance computing is a plus. Proficiency in office technologies like PowerPoint, Confluence, LaTeX, Word, and Excel.

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