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Polymarket is the world's fastest-growing prediction market, enabling users to trade on outcomes across politics, economics, sports, culture, and current affairs. The platform operates as a peer-to-peer marketplace with no centralized house, aggregating diverse opinions into transparent, market-based probabilities. With $115B traded to date and rapid adoption as an alternative news source, Polymarket is seeking a Senior Data Scientist/Analyst to serve as the analytical backbone of the product team.
In this role, you will own product insights end-to-end: defining success metrics, designing experiments to test them, and communicating results to stakeholders. You'll partner closely with product managers, designers, and engineers to determine whether shipped features actually work. This requires both strong analytical skills and genuine product instincts—you'll form hypotheses about user behavior, validate them rigorously, and push back when data doesn't support narratives.
Key responsibilities include designing and executing A/B tests from conception through analysis, building funnel and behavioral analyses to understand user journeys (discovery, first trade, retention), and surfacing friction points and opportunities for product direction. You'll own event tracking specifications, collaborate with analytics engineers to build scalable datasets, and document analyses clearly so any team member can understand and build upon your work.
You'll operate in a fast-moving environment where releases happen frequently and insights are needed quickly. The role demands comfort with ambiguity, the ability to scope vague questions into rigorous analyses, and strong communication skills to translate complex findings for non-technical audiences.
Required: 7+ years in product analytics, data science, or similar roles supporting consumer or trading products. Expert-level SQL proficiency, deep A/B testing experience in production, strong product sense, hands-on event tracking and instrumentation expertise, and the ability to work in high-ambiguity, fast-paced settings. Preferred: background in causal inference, quasi-experimental methods, or predictive modeling (Python/R); experience with trading, marketplace, or two-sided products.
