The company aims to build AI that builds teams by developing a platform that trains large-scale models to predict on-the-job performance more accurately than traditional interviews. The data scientist will focus on shipping analyses and experiments that drive key product metrics such as match quality, time-to-hire, candidate experience, and revenue.
Requirements
- Proficiency in SQL and Python for analysis.
- Strong grounding in statistics, experiment design, and causal inference.
- Familiarity with dbt and dashboarding tools (Hex, Mode, Looker).
- Exposure to marketplace, search, or recommendation metrics.
- Interest in LLM/agent evaluation, retrieval, and ranking.
Responsibilities
- Define north-star and feature-level metrics for ranking, interview analytics, and payout systems.
- Design and execute A/B tests and quasi-experiments to guide rapid product decisions.
- Build dashboards and lightweight data models to enable teams to self-serve insights.
- Partner with engineers to improve event instrumentation, data quality, and latency.
- Prototype models (baselines, gradient boosting, etc.) to enhance matching and scoring systems.
- Support LLM-powered agent evaluation with rubrics, human-in-the-loop studies, and monitoring frameworks.
Other
- 0–2 years of experience in data science, analytics, or related fields.
- Bachelor’s degree in a quantitative discipline (or equivalent).
- Ability to communicate insights clearly to engineers, PMs, and leadership.
- Strong fundamentals with projects worth showcasing.
- Rapid iteration — ability to frame questions, test, and ship in days.
- Equal focus on clarity of communication and technical rigor.
