What You Re Expected To Do

In your first year you will deliver analyses and experiments that improve core product metrics—match quality, time-to-hire, candidate experience, and revenue. You will:

Define north-star and feature-level metrics for our ranking, interview analytics, and payouts systems.
Design and run A/B tests and quasi-experiments; translate results into product decisions within the same week.
Develop source-of-truth dashboards and concise data models to enable teams to self-serve answers.
Collaborate with engineers to instrument events and improve data quality and processing latency from ingestion to insight.
Prototype lightweight models (from baseline methods to gradient-boosted trees) to improve matching and scoring.
Contribute to the evaluation of LLM-powered agents by designing evaluation rubrics, human-in-the-loop studies, and guardrail canaries.

You will thrive here if

You have solid fundamentals in statistics, SQL, and Python, and maintain projects you are prepared to demonstrate. You iterate quickly—frame the question, test, and implement solutions within days—and place as much value on clear communication as on statistical rigor. Familiarity with LLM evaluation, retrieval, and ranking is a plus; you will have the opportunity to learn alongside colleagues with experience at Jane Street, Citadel, Databricks, and Stripe.

Qualifications

0–2 years in data science/analytics or a similar role; BS/BA in a quantitative field (or equivalent practical experience).
Strong SQL skills and Python for analysis; comfortable with experiment design and causal thinking.
Communicates clearly and concisely with engineers, product managers, and leadership; translates analysis into actionable recommendations.
Nice-to-haves: dbt, dashboarding (Hex/Mode/Looker), marketplace or search/recommendation metrics, LLM/agent evaluation.

Perks

Free Equinox membership
Health insurance

Skills: python,sql,models,analytics,data science,llm,data scientist

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