data
Posted Mar 25Data Scientist
at Replit
Foster City, United StatesRemote
Responsibilities
- YOU WILL: - Design and analyze marketing experiments across paid, lifecycle, and content channels; optimize CAC, LTV, and ROAS - Build multi-touch attribution and marketing mix models to understand what's driving growth - Synthesize customer signals — support tickets, social, reviews, CSAT — into automated intelligence that reaches the teams who need it - Build churn and retention models to identify at-risk users and inform lifecycle intervention strategies - Define and maintain customer segmentations and
Requirements
- Our mission is Autonomy for All — making programming accessible, collaborative, and powered by AI.
- You'll work across paid and organic channels, lifecycle marketing, customer feedback, social signals, and support data — and you'll use AI to do it at a scale that would be impossible manually.
- 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 - Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.) -
- experience using LLMs/AI tools in analytics workflows — not just prompting, but building automated systems - Track record of partnering cross-functionally with Marketing, Product, Engineering, Support, and Revenue/Sales teams — not just serving a single stakeholder PREFERRED QUALIFICATIONS -
- Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, Segment, etc.) - Background in growth analytics, marketing analytics, or conversion rate optimization at a SaaS or PLG company -
- Experience with marketing technology platforms (Google Analytics, Segment, Iterable, Salesforde) -
- Experience with attribution modeling, marketing mix modeling, or incrementality testing -
- Experience analyzing unstructured customer data (support tickets, reviews, social mentions) using NLP or LLM-based approaches - 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) -