Data Scientist

Mercor's mission is to organize human intelligence to power the AI economy. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development. Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $2 million a day.

Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.

In your first year you’ll ship analyses and experiments that move core product metrics, match quality, time-to-hire, candidate experience, and revenue. You’ll:

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  Define north-star and feature-level metrics for our ranking, interview analytics, and payouts systems.

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  Design/run A/B tests and quasi-experiments; turn results into product decisions the same week.

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  Build source-of-truth dashboards and lightweight data models so teams can self-serve answers.

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  Instrument events with engineers; improve data quality and latency from ingestion to insight.

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  Prototype quick models (from baselines to gradient boosting) to improve matching and scoring.

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  Help evaluate LLM-powered agents: design rubrics, human-in-the-loop studies, and guardrail canaries.

You have solid fundamentals (statistics, SQL, Python) and projects you’re proud to demo. You iterate fast, frame the question, test, and ship in day, and care as much about clarity of communication as you do about p-values. Curiosity about LLM evaluation, retrieval, and ranking is a bonus; you’ll learn alongside folks who’ve shipped at Jane Street, Citadel, Databricks, and Stripe.

• 0–2 years in data science/analytics or similar; BS/BA in a quantitative field (or equivalent work).

• Strong SQL; Python for analysis; comfort with experiment design and causal thinking.

• Communicates crisply with engineers, PMs, and leadership; turns analysis into action.

• Nice-to-haves: dbt, dashboarding (Hex/Mode/Looker), marketplace or search/recommendation metrics, LLM/agent evaluation.