Data Scientist, Product

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:

We're redefining how software is built and who gets to build it. Our mission is to achieve Autonomy for All: making programming accessible, collaborative, and powered by AI. To realize this vision, we need people who are obsessed with understanding how users experience our product and can turn that understanding into decisions that move the needle.

You'll directly impact Replit's growth by turning user behavior into actionable insights that shape product strategy, improve activation and retention, and drive sustainable revenue growth across our self-serve and enterprise segments.

Who You are

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. 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.

You will:

Design and analyze product experiments to evaluate feature launches, onboarding changes, and in-product interventions with rigorous statistical methodology.

Own the analytics for core product areas — Growth (activation, engagement, monetization & retention), feature adoption, product quality, AI agent effectiveness, and proactively surface insights that influence product roadmap decisions.

Build the analytical foundation for Replit's enterprise business, understanding team adoption patterns, workspace collaboration dynamics, and expansion signals that inform go-to-market and product strategy.

Develop predictive models to forecast frameworks to measure impact of features and new launches on user behavior, including likelihood to retain, convert, or expand, and embed those signals directly into product and growth workflows.

Examples of what you could do:

Design and analyze a complex multi-variant experiment testing pricing, onboarding, and feature gating simultaneously, navigating interaction effects and providing clear recommendations despite ambiguity.

Model the "aha moment" for new Replit users — identifying early behaviors most predictive of long-term retention — and work with the product team to redesign onboarding around those signals.

Analyze feature adoption and cohort behavior across user segments (students, hobbyists, professional developers, enterprise teams) to help PMs prioritize the roadmap with confidence.

Analyze enterprise team adoption patterns to identify what drives successful rollouts versus stalled deployments, and build the data models that power Replit's enterprise expansion playbook.

Required skills and experience:

Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or related field, OR equivalent real-world experience in data roles.

5+ years of experience in data science with a focus on product analytics, growth, or user behavior.

Strong SQL skills and experience working with large datasets, particularly event-level user behavior data, and designing ETL workflows using dbt.

Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.).

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.

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.