Senior Data Scientist, Product

Polymarket•New York, NY

About The Position

Polymarket is looking for a Data Scientist to be the analytical backbone of how the product team builds. You'll turn raw platform data into the numbers that shape what we ship — defining the metrics that tell us whether the product is healthy, sizing where the biggest opportunities are, and running the experiments that tell us whether a change actually worked. This is a product-first data science role. You'll sit inside the product team and partner directly with PMs, designers, and engineers, while working closely with the central Data Science and Data Engineering teams who own the indexed data store and metric definitions.

Requirements

4+ years in a data science or product analytics role, ideally driving impact for a consumer product with a fast, iterative development cycle
Strong SQL and proficiency in Python (or R) for analysis and modeling
Real experimentation expertise — you can design an A/B test, reason about power and bias, and apply causal-inference methods when a clean experiment isn't possible
Solid grounding in statistics and the judgment to know which method fits the question
Product sense — you can turn an ambiguous product question into the right analysis and translate the result into a concrete recommendation
Excellent communication — you can turn messy data into a clear, well-structured narrative for a non-technical audience
Comfort operating lean in an ambiguous, fast-moving environment where priorities shift and you own your work end-to-end

Nice To Haves

Experience with personalization, ranking, recommendation, or pricing/incentive systems
Familiarity with prediction markets, trading, crypto, or other real-money marketplaces
Track record of building experimentation practices, not just running individual tests

Responsibilities

Define, build, and maintain the dashboards that tell the product team how the product is performing — surface anomalies before anyone else notices them, and back up product goals with clear evidence
Quantify the opportunity behind proposed work and help the team set realistic, measurable KRs grounded in data rather than guesswork
Plan, instrument, and read out A/B tests and other causal analyses to measure the impact of feature iterations, and make crisp ship / iterate / kill recommendations
Partner on the bigger bets that live and die on data — personalization, ranking, and incentives — from framing the approach through to measuring the result
Produce clear, well-argued readouts for product and leadership, and make sure they land and change what we do