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Replit is seeking a founding Data Scientist for its Growth team to drive acquisition, activation, and personalization initiatives. You will own data science for major growth areas, partnering closely with Product and Engineering leadership from problem framing through launch and analysis.
Key responsibilities include:
- Owning data science for growth initiatives, partnering with PMs and engineers across the full lifecycle
- Designing experiments and metrics that enable fast decision-making despite long feedback loops (e.g., variance reduction techniques like CUPED, sequential testing, short-term proxy metrics validated against long-term retention and revenue)
- Establishing experimentation standards and guardrails for Replit's highest-traffic surfaces
- Applying quasi-experimental designs (difference-in-differences, synthetic control, regression discontinuity) where randomization isn't feasible
- Defining data science approaches for personalization and recommendations: problem framing, offline/online evaluation, and incremental lift measurement
- Modeling freemium and usage-based product economics: credit consumption, conversion, pricing behavior
- Developing user and project segmentation from behavioral and content signals (embeddings, clustering)
- Partnering with Data Engineering on shared metrics infrastructure and experimentation tooling
This is a high-impact role for someone who identifies problems and opportunities proactively, brings strong conviction, and influences senior leadership. You'll work in a fast-shipping environment (weekly cadence) where you need to make trustworthy decisions within days.
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 existing infrastructure
- Deep expertise in experimental design and statistical inference, including reliable decisions with short readout windows and slow-moving outcomes
- Track record of establishing experimentation standards or platforms adopted by multiple teams
- Expert-level SQL and Python
- Demonstrated ability to influence senior Product and Engineering leaders through analysis that changed what teams shipped
- Heavy, practical use of AI tools to accelerate your own work
PREFERRED:
- Experience building 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 quantitative field
BONUS:
- Multi-armed bandits or adaptive experimentation experience
- Familiarity with developer tools, AI products, or creator platforms
