Data Scientist, Growth

Replit•Foster City, CA

1d•$250,000 - $327,000•Onsite

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. 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.

Requirements

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

Nice To Haves

Comfortable building your own datasets in dbt
Pricing, packaging, or monetization analysis for usage- or credit-based products
International or mobile growth experience
MS or PhD in statistics, economics, computer science, or another quantitative field
Experience with multi-armed bandits or adaptive experimentation
Familiarity with developer tools, AI products, or creator platforms

Responsibilities

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