Data Scientist, Growth

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

You'll be one of the founding data scientist on Replit's Growth team, working with Product and Engineering leadership across acquisition, activation and recommender systems, and helping shape how Replit experiments.

This role is for someone who doesn't wait to be asked. You spot problems and opportunities before anyone else does, bring a strong point of view, and push it until it changes what we build.

You will:

Own data science for major growth areas, partnering with PMs and engineers from problem framing and sizing through launch and readout

Make fast decisions trustworthy: we ship weekly and need a decision within days, but retention takes weeks to show up. You'll design the experiments and metrics that close that gap; for example, variance reduction (CUPED), sequential testing, and short-term proxy metrics validated against long-term retention and revenue

Help set the experimentation standards teams rely on for Replit's highest-traffic surfaces: how metrics and guardrails are defined, and the checks that catch a broken test before it misleads anyone

Use quasi-experimental designs (difference-in-differences, synthetic control, regression discontinuity) where randomization isn't feasible, such as pricing changes and regional launches

Help define the data science behind an emerging personalization and recommendations workstream: problem framing, offline and online evaluation, and measuring incremental lift

Model the economics of a freemium, usage-based product: credit consumption, conversion, and how pricing and limits change behavior

Develop user and project segmentation from behavioral and content signals (e.g., embeddings and clustering) so growth analysis reflects what people are actually building

Partner with Data Engineering on a shared metrics layer and experimentation tooling

Required Skills and Experience

6+ years in data science, with significant time on growth or experimentation for high-scale prosumer, PLG, or self-serve products

Data science experience at a fast-growing startup, where you built metrics, datasets, and experimentation practices without much existing infrastructure

Deep expertise in experimental design and statistical inference, including reaching reliable decisions when readout windows are short and the outcomes that matter are slow

Experience establishing experimentation standards or platforms adopted by multiple teams

Expert SQL and Python

A track record of analysis that changed what a team shipped, and of influencing senior Product and Engineering leaders

Heavy, practical use of AI tools to move faster in your own work

Preferred Qualifications

Comfortable building your own datasets in dbt;

Pricing, packaging, or monetization analysis for usage- or credit-based products