M

Mercor

Automates candidate screening and matching

Data Science Intern

Data ScienceUpdated on Nov 11
InternshipSan Francisco, CA, USA$70 /hr

About the Job

Requirements

  • Pursue a degree in a quantitative field and be graduating in 2027, potentially 2028.
  • Have strong fundamentals in statistics, SQL, and Python.
  • Have experience with experiment design, causal reasoning, and data analysis.
  • Communicate clearly with engineers, product managers, and leadership.
  • Be curious about LLM evaluation, retrieval, ranking, or marketplace dynamics.
  • Be willing to work in person in San Francisco.

Responsibilities

  • Define north-star metrics and feature-level key performance indicators for ranking, interview analytics, and payouts.
  • Design A/B tests and quasi-experiments, and turn results into product decisions within days.
  • Build dashboards and lightweight data models so teams can self-serve insights.
  • Instrument events with engineers and improve data quality, observability, and latency.
  • Prototype models, from baselines to gradient boosting, to improve matching and scoring.
  • Evaluate LLM-powered systems through rubric design, human-in-the-loop experiments, and guardrail testing.
  • Analyze production datasets that affect ranking, hiring efficiency, candidate experience, and revenue, and deliver insights used by product and engineering.
  • Take ownership of assigned analyses and tooling with support from a named mentor.

Desired Qualifications

  • Experience with dbt and dashboarding tools.
  • Experience with recommendation or search metrics.
  • Experience with LLM and agent evaluation.

About the Company

Mercor

Automates candidate screening and matching

Mercor provides an AI-driven recruitment platform that automates key parts of hiring for both job seekers and employers. Job seekers upload their CVs and complete a 20-minute AI-led video interview, after which a proprietary language model generates detailed candidate profiles; employers receive automated resume screening, candidate matching, and initial interviews, with tailored AI interviews for specialized roles. The system uses a company-trained model to rank and recommend candidates by qualifications and predicted performance, and operates on a finder’s-fee revenue model with large-scale usage across software, operations, design, and other fields, including AI labs. Its goal is to speed up hiring while reducing bias and delivering better job-to-citizen matches.

Company size
201-500
Company stage
Series D
Total funding
$1.2B
Headquarters
Menlo Park, CA
Founded
2023

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