Data Analyst — MCT PathAI

Job description

About the role MCT PathAI scrapes 5,000+ jobs daily from Greenhouse, Lever, and Ashby, scores resumes across 10 dimensions, and filters by visa sponsorship — all running through a daily GitHub Actions pipeline on Supabase. The data is rich and the product is growing. We need an analyst to make sense of it. You'll work directly with the founder and product team to answer questions like: which job boards produce the highest-match roles? Where do users drop off? Which resume dimensions actually predict callbacks? Are our sponsorship filters accurate? What you'll do - Own the analytics layer: build and maintain dashboards for scraper health, match quality, user funnel, and retention - Run deep-dive analyses on resume-to-job matching performance and recommend model or product changes - Monitor the daily 7AM PDT pipeline, flag anomalies, and build data-quality checks - Partner with engineering on instrumentation — define the events, metrics, and schemas we need - Translate messy scraped data (Greenhouse/Lever/Ashby payloads) into clean, queryable tables - Build self-serve reporting so the team stops asking you for one-off pulls - Support GTM with cohort analysis, conversion metrics, and beta-user insights Tech stack SQL (Postgres / Supabase) · Python (Pandas, NumPy) · Streamlit or Metabase for dashboards · GitHub Actions · dbt (nice to have) · Looker Studio or Superset How to apply Send to connect@theteammc.com: 1. Resume or LinkedIn 2. A portfolio, GitHub, or a dashboard you've built 3. One paragraph: given a table of 5,000 daily job postings and user application data, what's the first metric you'd build and why? We review on a rolling basis and reply to every applicant who answers #3.

Minimum qualifications

• 2–4 years in a data analyst or analytics engineer role
• Expert SQL — CTEs, window functions, query optimization on Postgres
• Python for data work — Pandas, Jupyter, basic scripting
• Built and shipped dashboards that real stakeholders actually use (Metabase, Looker, Superset, Streamlit, or similar)
• Comfort with messy, semi-structured data (JSON payloads from APIs, scraped HTML)
• Strong product sense — able to turn a vague business question into a crisp metric
• Excellent written communication; can explain findings to non-technical founders
• Nice to have: experience with Supabase or Postgres as a primary data warehouse
• Nice to have: dbt or any transformation framework
• Nice to have: familiarity with recruiting, ATS, or HR-tech data (Greenhouse, Lever, Ashby)
• Nice to have: basic stats / A-B testing
• Nice to have: Python notebooks for exploratory ML (clustering, classification)

Preferred qualifications

• Client-facing delivery and cross-functional collaboration experience.
• Strong communication and stakeholder management skills.
• Experience owning end-to-end delivery outcomes.