Markets Data Scientist: Liquidity & Market Design

Unchain Data; New York (NY); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Competitive salary & equity

Unlimited PTO

Health, Vision, & Dental coverage

401k match

Hardware setup: MacBook Pro, display

Job summary

Polymarket is seeking a data scientist for the Markets team to own the analytical layer that informs listings, liquidity design, and how we reward liquidity providers. You will evaluate market health, model dynamics, and build infrastructure for scalable analysis that guides product decisions and risk management.

You will work cross-functionally with engineering and product, identify critical data questions, create repeatable reports, and deliver outputs that drive action rather than just

Qualifications

• Strong Python and SQL for data analysis and modeling.
• Deep understanding of market structure and liquidity dynamics.
• Experience delivering analyses end-to-end with clear stakeholders outputs.

Responsibilities

• Model liquidity dynamics across markets to inform structure and rewards.
• Build and maintain recurring dashboards on market health metrics.
• Audit new market listings using historical data to guide approvals or adjustments.
• Analyze underperforming markets to diagnose root causes and timing issues.
• Collaborate with Markets team weekly to scope data questions and deliver actionable outputs.
• Develop frameworks to evaluate what makes a market work across listings.

Skills

Python

SQL

Market structure

End-to-end analysis

Cross-functional

Prediction market experience

DeFi / AMM familiarity

Education

Bachelor's in CS/Math/Stats

Tools

Jupyter

Pandas/NumPy

Job description

Polymarket is seeking a data scientist for the Markets team to own the analytical layer that informs listings, liquidity design, and how we reward liquidity providers. You will evaluate market health, model dynamics, and build infrastructure for scalable analysis that guides product decisions and risk management.

You will work cross-functionally with engineering and product, identify critical data questions, create repeatable reports, and deliver outputs that drive action rather than just