Quantitative Risk Analyst — Derivatives & Clearing

Polymarket is the world's largest prediction market platform. We enable individuals to express views on real-world events by trading on outcomes across politics, economics, sports, culture, and current affairs. Built as a peer-to-peer marketplace with no centralized "house," Polymarket aggregates diverse opinions into transparent, market-based probabilities that reflect collective expectations about the future.

We're growing fast — both in terms of volume ($21B traded in 2025) and adoption as an alternative news source. Our ambition is to become a ubiquitous beacon of truth in global media and we need your help adding fuel to the fire.

Polymarket is hiring a Quantitative Risk Analyst to design and implement enterprise-scale risk models at the heart of our clearing operation. You'll own models for market risk, volatility and correlation of derivatives, stress testing, and automated liquidation — the systems that keep the platform solvent and users protected in fast-moving markets.

This is a hands-on role: you'll be building models in production code, not just specifying them. We expect you to work fluently with AI tools for development and research — and to be the skeptic in the room, pressure-testing AI-generated models and code against well-established risk frameworks before anything ships.

• 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 — trigger thresholds, liquidation waterfalls, and safeguards against cascading liquidations

• Use AI tools extensively to accelerate model development, coding, and research — and rigorously validate AI outputs against established risk models before deployment

• 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

• 5–7 years of quantitative risk experience at a clearinghouse, exchange, prime broker, trading firm, or similar

• Proven expertise designing and implementing risk models at enterprise scale — production systems, 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 mechanics in a clearing context

• Strong fluency with AI-assisted development and coding, paired with the judgment to pressure-test AI outputs against well-established risk models and catch what looks plausible but is wrong

• Expert-level Python (NumPy, pandas, SciPy; solid software engineering practices)

• Advanced degree in a quantitative field (math, statistics, physics, financial engineering, CS) or equivalent experience

• Strong mathematical foundation in stochastic calculus and linear algebra

• (Plus) C# and/or C++ for performance-critical or production systems

• (Plus) Familiarity with crypto market structure, perpetuals, or prediction markets

• (Plus) Experience with CCP risk frameworks (CPMI-IOSCO PFMI, default management, margin methodology)

• (Plus) Experience building real-time risk systems

• Competitive salary & equity

• Unlimited PTO

• Full Health, Vision, & Dental coverage

• 401k match

• Hardware setup: new MacBook Pro, big display, & accessories

Base salary range: $210,000 to $24,000, plus equity and benefits.

This is a good-faith range for the level we're hiring at. Experience varies widely within a title here, so if your expectations fall outside it, reach out anyway. We're always happy to talk it through.