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Polymarket, the world's largest prediction market platform, is seeking a Quantitative Risk Analyst to design and implement enterprise-scale risk models for its clearing operation. You will own the models that keep the platform solvent and protect users during volatile market conditions.
In this hands-on role, you'll build production risk models covering market risk, margin, counterparty exposure, volatility, correlation, stress testing, and automated liquidation logic. You'll design volatility and correlation models for derivatives with calibration and backtesting; develop comprehensive stress-testing frameworks including historical scenarios, hypothetical shocks, and reverse stress tests; and create auto-liquidation systems with trigger thresholds and safeguards against cascading failures.
You'll leverage AI tools extensively to accelerate model development and research, but critically—you'll be the skeptic in the room, rigorously validating AI-generated models and code against established risk frameworks before deployment. You'll monitor model performance in production, investigate breaks, and iterate quickly. Collaboration with engineering, trading, and product teams is essential to embed risk controls into platform architecture. All work must be documented to audit-ready standards.
Required: 5–7 years of quantitative risk experience at a clearinghouse, exchange, prime broker, or trading firm. You must have proven expertise designing and implementing production-scale risk models (not just research prototypes), deep experience modeling volatility, correlation, option skews, and option pricing for derivatives, and hands-on experience with market risk modeling, stress testing, and auto-liquidation mechanics in a clearing context. Expert-level Python (NumPy, pandas, SciPy) with solid software engineering practices is essential. You'll need an advanced degree in a quantitative field (math, statistics, physics, financial engineering, CS) or equivalent, plus a strong foundation in stochastic calculus and linear algebra. Strong fluency with AI-assisted development and the judgment to pressure-test AI outputs is critical.
Plus factors: C# and/or C++ for performance-critical systems, familiarity with crypto market structure or prediction markets, experience with CCP risk frameworks (CPMI-IOSCO PFMI, default management, margin methodology), and experience building real-time risk systems.
