Data Scientist, Product

Replit Replit · Enterprise · Foster City, CA · Hybrid · Engineering

Data Scientist, Product at Replit, focusing on analyzing user behavior to shape product strategy, improve activation and retention, and drive revenue growth. The role involves designing and analyzing product experiments, owning analytics for core product areas, building the analytical foundation for the enterprise business, and developing predictive models. The candidate is expected to leverage AI tools extensively in their workflow while maintaining high standards for output quality.

What you'd actually do

  1. Design and analyze product experiments to evaluate feature launches, onboarding changes, and in-product interventions with rigorous statistical methodology.
  2. 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.
  3. 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.
  4. 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.

Skills

Required

  • 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.

Nice to have

  • 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.

What the JD emphasized

  • rigorous statistical methodology
  • AI agent effectiveness
  • AI tools extensively
  • high standards for output quality

Other signals

  • AI agents and tools
  • AI agent effectiveness
  • AI-powered analytical tools