Data Scientist (Growth)

Replit; United States; Remote; USD 140,000 - 180,000; Full time

Benefits offered by this job

Flexible Work Hours

Remote-First Working Environment

Monthly Expenses Stipend

Health, Dental, Vision, Life Insurance

401k Program

Flexible PTO (2 Weeks Minimum) + Holyd

Job summary

Replit is seeking a founding data scientist on the Growth team to collaborate with Product and Engineering on acquisition, activation, and recommender systems. You will shape experiments and play a critical part in growth strategy across major surfaces.

You’ll design experiments with quick decision cycles, build metrics, and partner with teams to ensure tests reflect long-term retention and revenue impact. A strong background in growth analytics and experimental methods is essential.

Qualifications

• MS or PhD in statistics, economics, computer science, or another quantitative field.
• Experience with growth or experimentation for high-scale prosumer, PLG, or self-serve products.
• Expert SQL and Python.
• Experience with multi-armed bandits or adaptive experimentation.

Responsibilities

• Own data science for major growth areas, partnering with PMs and engineers from problem framing and sizing through launch and readout.
• Design experiments and metrics to close gaps between short-term signals and long-term retention/revenue.
• Define data science behind personalization and recommendations workstreams.

Skills

Experimental design

Statistical inference

SQL

Python

Growth analytics

A/B testing

CUPED

Economics modeling

Education

MS or PhD in statistics, economics, computer science, or another quantitative field

Tools

dbt

Job description

• 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
• 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

Benefits

• Flexible Work Hours
• Remote-First and Autonomous Working Environment
• Monthly Expenses Stipend
• Short Term and Long Term Disability
• Health, Dental, Vision, and Life Insurance
• Home Office Set-Up Stipend
• Annual Company/Team Offsites (4/year)
• 401k Program
• Flexible PTO (2 Weeks Minimum Required) + Holidays
• Commuter Benefits
• Parental and Baby Bonding Leave
• Equity

Heavy, practical use of AI tools to move faster in your own workA track record of analysis that changed what a team shipped, and of influencing senior Product and Engineering leadersData science experience at a fast-growing startup, where you built metrics, datasets, and experimentation practices without much existing infrastructure6+ years in data science, with significant time on growth or experimentation for high-scale prosumer, PLG, or self-serve productsDeep expertise in experimental design and statistical inference, including reaching reliable decisions when readout windows are short and the outcomes that matter are slowExperience establishing experimentation standards or platforms adopted by multiple teamsExpert SQL and PythonWe welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgroundsMS or PhD in statistics, economics, computer science, or another quantitative fieldExperience with multi-armed bandits or adaptive experimentationInternational or mobile growth experienceFamiliarity with developer tools, AI products, or creator platformsComfortable building your own datasets in dbtPricing, packaging, or monetization analysis for usage- or credit-based products