> Markdown version of [/jobs/ext/3952982-senior-data-scientist-analyst-institutional](https://www.wearedevelopers.com/jobs/ext/3952982-senior-data-scientist-analyst-institutional). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist / Analyst, Institutional - **Company:** POLYMARKET CLEARING LLC - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $200,000 - $300,000 - **Contract:** Permanent contract - **Skills:** Training Data, Data Analysis, Business Logic, Python (Programming Language), Program Design Languages, Blockchain, SQL Databases - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-data-scientist-analyst-institutional-new-york-ny--7f9c98ab-e88e-415f-864c-856ec4f29a97 ## About the Role Data Science, Data Analysis, Politics, Sports, Economics, Data Science, Data Analysis, Analysis Skills, Negotiation Skills, Training Data Sets, * 7+ years in analytics, trading, or counterparty analysis, ideally at an exchange, trading venue, brokerage, or market making firm * Expert SQL - you can work directly against order book and transaction-level data without supervision * Deep understanding of how professional participants make money - spread capture, adverse selection, inventory risk, and the economics of providing liquidity * Credibility in a technical conversation with a sophisticated counterparty - you can hold your own with a firm's own quants * Commercial instinct - you can look at a relationship and articulate what it is worth to us and where the leverage sits * Strong communication and discretion, including with sensitive counterparty information and in front of external partners * Comfortable operating in a fast-moving environment where business logic changes frequently and you need to keep pace * (Plus) Experience with institutional pricing, fee tiers, or rebate program design * (Plus) Familiarity with on-chain data or blockchain analytics * (Plus) Python or R for deeper statistical work on trading behavior * (Plus) Experience in fintech, crypto, prediction markets, or other data-intensive financial products ## Description Polymarket is looking for a Senior Data Scientist / Analyst to own the analytics behind our institutional relationships - the market makers, large takers, and brokerages that account for a disproportionate share of platform volume. You'll be the person who understands each of these counterparties in depth: how they trade, how they make money with us, and whether the relationship is getting healthier or weaker. This is a role for someone comfortable on both sides of the conversation - rigorous enough to analyze flow at the order level, and commercial enough to sit in a partner review and explain what the numbers mean for the terms of the relationship. And you'll do it in a fast-moving environment where the institutional side of the business is being built as you go. What You'll Do * Own the analytics on our largest makers, takers, and brokerages, and be the internal source of truth on how each relationship is performing * Build the account-level view of every major counterparty - volume, flow composition, spread capture, fee and rebate economics, and how each has trended over time * Monitor counterparty health and surface concentration risk, deteriorating engagement, or churn signals early enough for the team to act on them * Analyze trading behavior to understand how each participant actually makes money on our platform, and whether the terms we have given them still make sense * Support the institutional team through reviews and negotiations with the analysis behind them - what a counterparty is worth, what they are asking for, and what we should be willing to give * Quantify how institutional flow affects the broader market, including liquidity, spreads, and the experience of retail traders on the other side of it * Work with analytics engineers to turn recurring relationship reporting into modeled datasets, so account reviews do not start from scratch every quarter * Own documentation end-to-end - writing clearly and thoroughly enough that anyone on the team can pick up your analysis without friction