Data Scientist (Product)

About the role

Join Replit, a leading collaborative coding platform, as a Data Scientist. In this role, you will design and analyze product experiments, own analytics for core product areas, build the analytical foundation for Replit's enterprise business, and develop predictive models to measure the impact of features and new launches on user behavior. You will have the opportunity to work in a remote-first and autonomous environment, with flexible work hours and a range of benefits including health insurance, a home office stipend, and equity. Key missions: Concevoir et analyser des expériences produit pour évaluer les lancements de fonctionnalités, les changements d'intégration et les interventions dans le produit.. Posséder l'analyse des domaines de produit clés, y compris la croissance, l'adoption des fonctionnalités et la qualité du produit.. Développer des modèles prédictifs pour prévoir les cadres de mesure de l'impact des fonctionnalités et des nouveaux lancements sur le comportement des utilisateurs. Profile: - 5+ years of experience in data science with a focus on product analytics, growth, or user behavior - Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or related field, OR equivalent real-world experience in data roles - Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.) - 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 - Strong SQL skills and experience working with large datasets, particularly event-level user behavior data, and designing ETL workflows using dbt - 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 - Experience analyzing freemium or usage-based pricing models - Experience with causal inference methods (difference-in-differences, synthetic control, propensity score matching) - Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.) - Understanding of developer tools, collaborative coding environments, or technical products - Experience at a PLG company with a self-serve funnel and freemium or usage-based pricing model - You're a data scientist who moves fast and goes deep. You can spin up an analysis in hours that would take others days; not by cutting corners, but because you've built the intuition and technical toolkit to get to the right answer quickly - Experience designing ETL workflows and data pipelines using dbt or similar tools - Familiarity with customer data platforms (CDPs) and event tracking implementation - Experience working directly embedded with product teams in an agile environment - You've built or contributed to AI-powered analytical tools, automation, or novel measurement approaches - You're the person who digs past the top-line number to find the confound, questions whether the metric actually measures what people think it does, and pressure-tests your own work before anyone else sees it. You treat experimentation as a craft, not a checkbox. You've felt the pain of underpowered tests and novelty effects, and you have the judgment to get it right - You use AI agents and tools aggressively to multiply your output - writing code, exploring data, generating hypotheses, but you treat every AI-assisted output as a draft, not a deliverable - You know what good analysis looks like and you won't ship anything that doesn't meet that bar. The result is that you operate at a speed and depth that most DS teams can't match