Senior Data Scientist

About the role

The will own the design and execution of detection strategies that protect the integrity of our prediction markets. You will investigate patterns that indicate abuse such as fake and duplicate signups, farmed bonuses, collusive trading, and market manipulation across political, economic, sports, and cultural events. This role requires you to translate complex behavioral signals into actionable controls in partnership with product and compliance teams. You will be responsible for turning one-off investigations into scalable monitoring systems that automatically surface emerging abuse patterns. A key part of the role is quantifying the financial exposure of each vector and prioritizing efforts based on actual cost rather than perceived severity. You will also own the documentation of detection logic, including thresholds and rationale, ensuring clarity for compliance and engineering stakeholders. The position demands comfort with adversarial problems where bad actors actively try to hide their behavior, and success is measured by your ability to reduce abuse without degrading user experience.

Key facts

What you'll do

• Investigate and quantify abuse across the funnel, including multi-accounting and fake signups, bonus and promotion farming, wash trading, collusion, and market manipulation using a combination of on-chain, device, and behavioral signals.
• Build detection logic that separates genuine users from coordinated behavior by analyzing clusters of accounts and their interaction patterns rather than relying on any single data point.
• Turn one-off investigations into monitoring, including recurring reporting and alerting that surfaces new patterns without someone having to manually search for anomalies.
• Size the financial exposure of each abuse vector so the team can prioritize remediation by actual cost impact instead of perceived risk or alarm level.
• Partner with product on controls at critical friction points such as onboarding, verification, and bonus eligibility, and measure their effectiveness while ensuring legitimate user access is not degraded.
• Support compliance with detailed analysis behind investigations, escalations, and regulatory reporting, maintaining clear documentation that can withstand audit scrutiny.
• Work with analytics engineers to promote detection logic into the modeled layer so it runs reliably in production instead of living in temporary notebooks or scripts.
• Own documentation end-to-end, including the thresholds and rationale behind detection rules, written clearly enough that compliance or any engineer can follow the logic without you present.

Requirements

• 7+ years in risk, fraud analytics, trust and safety, or a similar investigative analytical role with a demonstrated history of working on adversarial systems.
• Expert SQL capabilities, enabling you to pursue hypotheses across large behavioral datasets without supervision or excessive reliance on external assistance.
• Strong pattern recognition instinct, allowing you to look at a cluster of accounts and articulate what they share and why it is unlikely to be coincidence based on timing, structure, or activity.
• Experience building detection rules or models, with honesty about the tradeoff between false positives and missed abuse and the ability to communicate that balance to stakeholders.
• Sound judgment about user impact, understanding that every control has a cost to legitimate users and being able to weigh the tradeoffs in high-stakes environments.
• Comfort working alongside compliance teams and the discretion to handle sensitive findings appropriately in regulated contexts.
• Comfortable operating in a fast-moving environment where business logic changes frequently and you need to keep pace while maintaining analytical rigor.
• (Plus) Experience with on-chain analysis, wallet clustering, or blockchain forensics, particularly as it relates to tracing behavior across decentralized systems.
• (Plus) Experience with trade surveillance, market manipulation detection, or anti-money laundering practices from financial or crypto environments.
• (Plus) Statistical or machine learning background, including anomaly detection, graph analysis, or clustering implemented in Python or R.
• (Plus) Experience in fintech, crypto, prediction markets, or other data-intensive financial products where risk decisions directly affect platform integrity.

Nice to have

The preferred items in this section are explicitly stated as advantageous but not required, and they focus on technical depth in blockchain analysis, trade surveillance, and advanced modeling approaches.

Practical notes

Hours are full_time during standard business operations in New York. Travel is not expected as part of the role. No visa sponsorship is available for this position at this time. There are no published deadlines for this opening.