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Founding Forward Deployed Data Scientist

Career Mentors, LLC · San Francisco, CA, United States

Data Science / AI / Machine LearningQuick applyfull-time28 days ago

About The Role

Founding Forward Deployed Data Scientist

  • San Francisco, CA (4 days/week in-office)
  • Full-time | Early-stage startup (Seed–Series A)
  • Salary: $200-250K + up to 3% equity

About Our Client

Writing code is easier than ever. Building software users actually love is still painfully hard.

Our client is an AI product engineer that watches every user session and listens to every piece of feedback — support tickets, sales calls, and conversations — and automatically turns that signal into clear, prioritized product decisions. Instead of digging through events and scattered feedback, product teams get a complete picture of what to build next and why , with the ability to launch PRs or run experiments directly from insights.

They are building for AI-native product teams and closing the loop between user behavior and code.

The Role

This is not a reporting or support analytics role.

As a Founding Forward Deployed Data Scientist , you will own product analytics end-to-end — from raw user behavior to executive-level insights that directly shape what gets built. You’ll work directly with founders, customers, and product engineers, often embedded in high-stakes product decisions.

You will build analytics frameworks, causal measurement systems, and internal tools that scale with the product — and you’ll ship them.

If you prefer clean specs, narrow scopes, or only running experiments designed by others, this role is not for you.

What You’ll Do

  • Own analytics for user-facing product features from discovery to impact
  • Synthesize behavioral data and qualitative feedback into clear product recommendations
  • Work directly with customers and executives to understand needs and deliver insights
  • Design and run causal measurement : A/B tests, diff-in-diff, matched markets, power analysis
  • Build and ship analytics frameworks and internal tools used by product and growth teams
  • Partner closely with engineering to connect insights → product changes → experiments
  • Operate comfortably in ambiguity and take full ownership of outcomes

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