Product Data Scientist owning growth and enterprise analytics for an AI-native coding platform

Replit is hiring a product-focused Data Scientist to own analytics across activation, retention, monetization, and its growing enterprise segment. The role blends rigorous experimentation design with hands-on SQL/Python work and expects candidates to actively leverage AI tools while holding a high bar on analytical quality.

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What this interview tests

A/B test design (power, sample sizing, novelty effects, experiment interference)
SQL + dbt-style ETL over large event-level datasets
Python analytics stack (pandas, scikit-learn, statsmodels)
Causal inference methods (diff-in-diff, synthetic control, propensity score matching)
Self-serve/PLG growth analytics vs. enterprise adoption analytics
Judgment on AI-assisted analysis quality

Common question themes

Design a multi-variant experiment with interacting factors (pricing, onboarding, feature gating) and explain how you'd disentangle interaction effects

Define and validate an 'aha moment'/activation signal from event data for a new user segment

Critique a metric that looks good on the surface but has a confound

Explain a causal inference method you'd use to measure a feature's true impact absent a clean A/B test

Analyze enterprise team adoption patterns to distinguish successful rollouts from stalled ones

Describe how you use AI tools in your analytical workflow while maintaining rigor

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