Data Scientist

About the role

Join Replit, a company on a mission to make programming accessible and collaborative. As a Data Scientist, you will be responsible for understanding customer behavior across various touchpoints and turning data into actionable insights. You will work across multiple channels, build attribution models, synthesize customer signals, and optimize marketing strategies. This role requires strong analytical skills, experience with data science and marketing analytics, and a passion for using AI to drive growth. Key missions: Conception et analyse d'expériences marketing, optimisation du CAC, LTV et ROAS.. Construction de modèles d'attribution multi-touch et de mix marketing pour comprendre les moteurs de croissance.. Syndication des signaux clients, intégration des retours d'expérience et des données comportementales. Profile: - 6+ years of experience in data science with a focus on marketing, growth, or customer analytics - Strong SQL skills and experience with large-scale event-level user behavior data; experience designing ETL workflows using dbt - Experience building dashboards and visualizations (Hex, Looker, Tableau, Mode, or similar) —> ideally automating them - Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.) - Track record of partnering cross-functionally with Marketing, Product, Engineering, Support, and Revenue/Sales teams — not just serving a single stakeholder - Experience designing and analyzing A/B tests with statistical rigor (sample sizing, significance testing, causal inference) - Demonstrated experience using LLMs/AI tools in analytics workflows — not just prompting, but building automated systems - Background in growth analytics, marketing analytics, or conversion rate optimization at a SaaS or PLG company - Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, Segment, etc.) - Experience with marketing technology platforms (Google Analytics, Segment, Iterable, Salesforde) - Experience analyzing unstructured customer data (support tickets, reviews, social mentions) using NLP or LLM-based approaches - Experience with attribution modeling, marketing mix modeling, or incrementality testing - Experience with support analytics, CSAT analysis, or customer experience measurement - Understanding of PLG motions and self-serve conversion funnels - Experience analyzing freemium or usage-based pricing models - Understanding of developer tools, collaborative coding environments, or technical products - Experience with causal inference methods (difference-in-differences, synthetic control, propensity score matching) - Experience building AI agents or automated analytical pipelines using LLM APIs