Quantitative Risk Analyst
US$210,000 – US$240,000 / yr base salary
Posted 26 Aug 2026
Advertised as “Quantitative Risk Analyst — Derivatives & Clearing”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5–7 yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$210,000 – US$240,000 / yr |
|---|
Field of studyComputer Science, Financial Engineering, Mathematics, Physics, Statistics
Study levelMaster's
About Polymarket
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.
About the Role
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.
What You'll Do
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Design, implement, and maintain enterprise-scale risk models covering market risk, margin, and counterparty exposure for a clearing organization
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Build volatility and correlation models for derivatives, including calibration, backtesting, and ongoing model validation
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Develop and run stress-testing frameworks: historical scenarios, hypothetical shocks, and reverse stress tests
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Design and tune auto-liquidation logic — trigger thresholds, liquidation waterfalls, and safeguards against cascading liquidations
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Use AI tools extensively to accelerate model development, coding, and research — and rigorously validate AI outputs against established risk models before deployment
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Monitor model performance in production, investigate breaks, and iterate quickly
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Partner with engineering, trading, and product teams to embed risk controls into platform architecture
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Document model assumptions, limitations, and validation results to an audit-ready standard