Quant Risk Analyst - Derivatives & Clearing

About the role

Polymarket, a prediction market platform, is hiring a mid-level quantitative risk analyst to design and implement enterprise-scale risk models at the heart of its clearing operation. The role covers market risk, volatility and correlation of derivatives, stress testing and automated liquidation, and is based in the New York City office, in person.

Key responsibilities

• Design, implement and maintain enterprise-scale risk models covering market risk, margin and counterparty exposure for a clearing organization.
• Build volatility and correlation models for derivatives, including calibration, backtesting and ongoing model validation.
• Develop and run stress-testing frameworks: historical scenarios, hypothetical shocks and reverse stress tests.
• Design and tune auto-liquidation logic, including trigger thresholds, liquidation waterfalls and safeguards against cascading liquidations.
• Use AI tools to accelerate model development and coding, then rigorously validate AI outputs against established risk models (VaR/ES, SPAN-style margining, GARCH-family volatility models, standard correlation frameworks).
• Monitor model performance in production, investigate breaks and iterate quickly.
• Partner with engineering, trading and product teams to embed risk controls into platform architecture.
• Document model assumptions, limitations and validation results to an audit-ready standard.

Required profile

• 5-7 years of quantitative risk experience at a clearinghouse, exchange, prime broker, trading firm or similar.
• Proven track record of designing and implementing risk models at enterprise scale, not just research prototypes.
• Deep experience modeling volatility, correlation, option skews and option pricing at scale for trad-fi derivatives, perpetuals and fully collateralized event contracts.
• Hands-on experience with market risk modeling, stress testing and auto-liquidation in a clearing context.
• Advanced degree in a quantitative field (math, statistics, physics, financial engineering, CS) or equivalent experience.
• Strong mathematical foundation in stochastic calculus and linear algebra.

Required skills

• Expert-level Python (NumPy, pandas, SciPy) with solid software engineering practices.
• Fluency with AI-assisted development and coding, with the judgment to pressure-test AI outputs against established risk models.
• Nice to have: C# and/or C++, familiarity with crypto market structure, perpetuals or prediction markets, CCP risk frameworks (CPMI-IOSCO PFMI, default management, margin methodology), and experience building real-time risk systems.