Data Scientist, Replit

• 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
• Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.)
• Experience designing and analyzing A/B tests with statistical rigor (sample sizing, significance testing, causal inference)
• Experience building dashboards and visualizations (Hex, Looker, Tableau, Mode, or similar) —> ideally automating them
• Demonstrated 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

Desirable

• Replit is redefining how software is built and who gets to build it. Our mission is Autonomy for All — making programming accessible, collaborative, and powered by AI
• This role owns how Replit understands its customers across every touchpoint. You'll build the analytics and intelligence layer that spans marketing performance, customer signals, and support — turning massive volumes of behavioral data, feedback, and interaction signals into insights that drive growth, retention, and revenue
• This isn't a traditional marketing analytics seat. 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. You'll build agents and automated systems that deliver insights directly to the teams that need them, not just dashboards that sit untouched
• 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 personas that drive targeting, messaging, and product decisions
• Build the analytical foundation for Voice of the Customer — connecting qualitative feedback signals to quantitative behavior data at scale
• Detect emerging product issues and bugs faster by surfacing support signal early enough to shape engineering priorities
• Optimize automation and deflection to reduce support load and improve self-serve resolution rates
• Build the measurement foundation to fully optimize ROI across all support activities
• Use LLMs and agentic workflows to analyze unstructured data at scale and automate recurring analysis
• Create automated reporting that put key metrics to inform the company