Data Scientist, Product

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Design and analyze product experiments to evaluate feature launches, onboarding changes, and in-product interventions with rigorous statistical methodology.
Own the analytics for core product areas — Growth (activation, engagement, monetization & retention), feature adoption, product quality, AI agent effectiveness, and proactively surface insights that influence product roadmap decisions.
Build the analytical foundation for Replit's enterprise business, understanding team adoption patterns, workspace collaboration dynamics, and expansion signals that inform go-to-market and product strategy.
Develop predictive models to forecast frameworks to measure impact of features and new launches on user behavior, including likelihood to retain, convert, or expand, and embed those signals directly into product and growth workflows.

Design and analyze a complex multi-variant experiment testing pricing, onboarding, and feature gating simultaneously, navigating interaction effects and providing clear recommendations despite ambiguity.
Model the "aha moment" for new Replit users — identifying early behaviors most predictive of long-term retention — and work with the product team to redesign onboarding around those signals.
Analyze feature adoption and cohort behavior across user segments (students, hobbyists, professional developers, enterprise teams) to help PMs prioritize the roadmap with confidence.
Analyze enterprise team adoption patterns to identify what drives successful rollouts versus stalled deployments, and build the data models that power Replit's enterprise expansion playbook.

Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or related field, OR equivalent real-world experience in data roles.
5+ years of experience in data science with a focus on product analytics, growth, or user behavior.
Strong SQL skills and experience working with large datasets, particularly event-level user behavior data, and designing ETL workflows using dbt.
Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.).
Experience designing and analyzing A/B tests and experiments, including rigor around sample sizing, power analysis, significance testing, novelty effects, interference between experiments, and causal inference.
You leverage AI tools extensively in your own analytical workflow and can demonstrate how they make you more effective, while maintaining high standards for output quality.

Experience at a PLG company with a self-serve funnel and freemium or usage-based pricing model.
Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.).
Experience with causal inference methods (difference-in-differences, synthetic control, propensity score matching).
Experience designing ETL workflows and data pipelines using dbt or similar tools.

You've built or contributed to AI-powered analytical tools, automation, or novel measurement approaches.
Experience analyzing freemium or usage-based pricing models.
Understanding of developer tools, collaborative coding environments, or technical products.
Experience working directly embedded with product teams in an agile environment.
Familiarity with customer data platforms (CDPs) and event tracking implementation.