Data Scientist

Replit•Foster City, CA

17d•Hybrid

About The Position

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role 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.

Requirements

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

Nice To Haves

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 with support analytics, CSAT analysis, or customer experience measurement
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

Responsibilities

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