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Chamberlain Advisors · 1 day ago

Quantitative Risk QA Consultant

Chicago, IL

Contract

Onsite

Mid Level

$46/hr - $47/hr

Chamberlain Advisors is seeking a Quantitative Risk QA Consultant to support day-to-day activities within a Clearing and Risk Management organization. This role partners closely with Quantitative Risk and IT teams to support the development, analysis, testing, and validation of margin and risk models across multiple asset classes.

Staffing & Recruiting

Hiring Manager

Brittney Stephenson

Responsibilities

Execute code release testing for quantitative risk systems and models across scheduled releases

Perform historical data validation to ensure accuracy and completeness of model inputs

Validate margin models, stress testing frameworks, and portfolio-level risk calculations

Conduct portfolio back-testing and analyze model performance under historical and stressed scenarios

Collaborate with Quantitative Risk and IT teams to identify, research, and resolve model or data issues

Independently analyze complex problems, formulate solutions, and implement corrective actions

Produce clear, thorough documentation of testing methodologies, findings, and outcomes

Ensure testing results meet internal quality standards and regulatory expectations

Qualification

Quantitative risk modelingStatistical modelsProgramming languagesFinancial markets knowledgeAdvanced derivatives modelingProblem-solving skillsCommunication skillsDocumentation skills

Required

Bachelor or Master's in Computer Science, Financial Engineering, Financial Mathematics, Mathematics, Physics, or a related discipline

Strong quantitative and analytical background

Excellent programming, communication, and documentation skills

Knowledge of financial markets

Experience with programming languages such as C++/C#, R, VBA, and SQL is also required

Preferred

Knowledge in advanced quantitative risk modeling and knowledge of statistical models in risk management

Knowledge in advanced derivatives modeling and knowledge of volatility models

Preference will be given to candidates who can demonstrate the best practices in developing risk models like Historical VaR, Monte Carlo VaR, Multi-Factor Risk Models, Stressed VaR, Liquidity Risk models, etc