Senior Data Scientist / Analyst, Fraud & Risk

About Polymarket

Polymarket is the world's fastest growing prediction market. 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 ($115B traded to date) 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 looking for a Sr Data Scientist / Analyst, Fraud & Risk to proactively prevent and detect potential fraud and abuse on our platform, such as identity theft, account takeovers, farmed bonuses, collusive or manipulative trading, ACH fraud, and other behavior. You'll work closely with product and compliance, sitting between the data and the decisions about who gets to trade and under what conditions.

This is a role for someone who enjoys adversarial problems. The behavior you're looking for is actively trying not to be found, and the signal is usually in how accounts act together rather than in any single field. You'll be expected to build the detection, quantify the exposure, and make a clear recommendation about what to do about it. And you'll do it in a fast-moving environment where every new product opens new abuse vectors.

What You'll Do

• Proactively prevent and detect fraud and abuse across the funnel such as identity theft, account takeovers, bonus and promotion farming, collusive or manipulative trading and ACH fraud
• Build detection logic that separates genuine users from coordinated behavior, combining on-chain, device, and behavioral signals rather than relying on any one of them
• Turn one-off investigations into monitoring, including recurring reporting and alerting that surfaces new patterns without someone having to go looking
• Size the financial exposure of each abuse vector so the team can prioritize by what it actually costs rather than by how alarming it looks
• Partner with product on controls at the points of friction: onboarding, verification, bonus eligibility, and measure whether they worked without driving away legitimate users
• Support compliance with the analysis behind investigations, escalations, and regulatory reporting
• Continuously assess how bad actors are adapting to existing controls and develop new signals and detection strategies as fraud patterns evolve
• Work with analytics engineers to promote your detection logic into the modeled layer so it runs reliably instead of living in a notebook
• Own documentation end-to-end — including the thresholds and rationale behind your detection logic, written clearly enough that compliance or any engineer can follow it without you in the room

What We're Looking For

• 7+ years in risk, fraud analytics, trust and safety, or a similar investigative analytical role
• Expert SQL. You can pursue a hypothesis across large behavioral datasets without supervision
• Pattern recognition instinct. You can look at a cluster of accounts and articulate what they share and why it is unlikely to be coincidence
• Experience building fraud detection rules or models, setting thresholds, evaluating precision and recall, monitoring performance, and adapting controls as risks evolve
• Sound judgment about user impact. You understand that every control has a cost to legitimate users, and you can weigh the two
• Comfort working alongside Compliance, with sound judgment and discretion in handling sensitive findings.
• Experience integrating internal and external datasets to generate actionable insights; experience partnering with vendors on experimentation, analytics, and implementation a plus.
• Comfortable operating in a fast-moving environment where business logic changes frequently and you need to keep pace
• (Plus) Experience with on-chain analysis, wallet clustering, or blockchain forensics
• (Plus) Experience with trade surveillance, market manipulation detection, or AML
• (Plus) Statistical or machine learning background — anomaly detection, graph analysis, or clustering in Python or R
• (Plus) Experience in fintech, crypto, prediction markets, or other data-intensive financial products