Senior Data Scientist / Analyst, Risk

Polymarket•New York, NY

19h

About The Position

Polymarket is seeking a Senior Data Scientist / Analyst, Risk to identify and combat platform abuse, such as fake accounts, farmed bonuses, and manipulative trading. This role involves investigating abuse patterns, developing detection mechanisms, quantifying financial exposure, and collaborating with product and compliance teams to implement and measure controls. The position is ideal for someone who thrives on adversarial challenges in a fast-paced environment where new products introduce new abuse vectors.

Requirements

7+ years in risk, fraud analytics, trust and safety, or a similar investigative analytical role.
Expert SQL skills for hypothesis testing across large behavioral datasets.
Strong pattern recognition instincts to identify shared characteristics and unlikely coincidences among clusters of accounts.
Experience building detection rules or models, with an understanding of the trade-off between false positives and missed abuse.
Sound judgment regarding user impact and the ability to balance control costs with legitimate user experience.
Comfort working with compliance teams and handling sensitive findings with discretion.
Ability to operate effectively in a fast-moving environment with frequently changing business logic.

Nice To Haves

Experience with on-chain analysis, wallet clustering, or blockchain forensics.
Experience with trade surveillance, market manipulation detection, or AML.
Background in statistics or machine learning, including anomaly detection, graph analysis, or clustering in Python or R.
Experience in fintech, crypto, prediction markets, or other data-intensive financial products.

Responsibilities

Investigate and quantify abuse across the user funnel, including multi-accounting, fake signups, bonus/promotion farming, wash trading, collusion, and market manipulation.
Build detection logic that distinguishes genuine users from coordinated behavior, integrating on-chain, device, and behavioral signals.
Develop monitoring systems, including recurring reporting and alerting, to surface new abuse patterns proactively.
Quantify the financial exposure of different abuse vectors to aid in prioritization.
Partner with the product team to implement controls at key friction points (onboarding, verification, bonus eligibility) and measure their effectiveness without negatively impacting legitimate users.
Support the compliance team with analysis for investigations, escalations, and regulatory reporting.
Collaborate with analytics engineers to integrate detection logic into the modeled data layer for reliable execution.
Own end-to-end documentation of detection logic, including thresholds and rationale, ensuring clarity for compliance and engineering teams.