Data Scientist, Enterprise

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

Replit spreads through companies differently than it does through individuals. One person builds an app, colleagues start using it, a team forms, an admin rolls Replit out more widely, and eventually Sales gets involved. You'll be the data scientist for Replit's enterprise business, covering both the product and the go-to-market motion: how Replit spreads inside organizations, what keeps teams engaged after rollout, which features and signals drive expansion, and how all of that becomes pipeline, revenue, and a reliable forecast.

Experimentation is hard here. There are far fewer accounts than users, people within an organization influence each other, and admins control what their teams see. You'll figure out how to learn quickly and rigorously anyway. You'll work with Enterprise Product, Engineering, Sales, Customer Success, RevOps, and Finance, and your work succeeds when it changes a product, GTM, or investment decision.

You will:

Define how we measure the enterprise business end to end: account-level activation, breadth (share of seats active), depth (apps built, deployed, and shared), collaboration, consumption, and net revenue retention

Understand virality inside organizations: how Replit spreads from a first builder to teams through app sharing, invites, and viewers who become builders; quantify within-account network effects and find the levers that accelerate them

Explain post-engagement churn: why seats and accounts that engaged at rollout go dormant, separate seat-level decay from account-level risk, and turn it into account health models that give the product and Customer Success early warning and a clear next action

Identify the product behaviors and features that precede seat expansion and plan upgrades, and use them to build product-qualified account (PQA) models that route bottoms-up usage to Sales at the right moment

Measure adoption and value for enterprise features — admin and governance controls, security, data connectors, collaboration, and deploying to data platforms — and use it to inform what we build next

Design experiments for enterprise: account- and cluster-level randomization, interference within organizations, small samples and heavy-tailed account sizes, stratified and matched-pair designs, staged rollouts and long-running holdouts, synthetic control, and Bayesian or hierarchical models where data is sparse

Measure the incremental impact of GTM programs — pilots, onboarding, enablement, and sales-assist — with the same rigor as product launches

Own enterprise forecasting for pipeline, bookings, consumption, and expansion, and pressure-test the assumptions behind capacity and quota plans

Analyze pricing and packaging: seats vs. consumption, and how contract structure shapes usage

Build a trusted enterprise data layer that joins product telemetry, CRM, and billing data and models the org, workspace, team, and seat hierarchy, so Product, Sales, and leadership can self-serve — increasingly through AI agents rather than static dashboards

Bring a point of view to the enterprise roadmap and GTM strategy before you're asked

Required Skills and Experience

6+ years in product or GTM data science, with significant experience on B2B, collaboration, or multi-seat products

Hands-on experimentation where the unit of randomization differs from the unit of analysis: cluster randomization, interference, and small-sample designs

Strong retention and survival modeling, cohort analysis, and segmentation at both user and account level

Experience defining adoption, retention, and revenue metrics (activation, NRR, GRR, expansion) and getting the company to agree on them

Built scoring or forecasting models that Sales, Customer Success, or Finance actually used

Expert SQL, including messy CRM, billing, and product data joined at the account level; strong Python; comfortable building your own datasets in dbt

A track record of analysis that changed a product roadmap or GTM decision, and the confidence to push back on an attractive story when the evidence doesn't support it

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

Preferred Qualifications

Experience at a company that grew from bottoms-up adoption into enterprise sales (collaboration, productivity, or developer tools)

Consumption- or usage-based revenue models and PLG-to-sales motions (PQLs/PQAs, sales-assist)

Virality, network, or graph analysis of collaboration data

Fluency with Salesforce data and RevOps tooling

Modern data stack: BigQuery, dbt, Hex

Bonus Points

Experience with enterprise admin, security, or governance product surfaces

Built AI agents for GTM teams (account research, deal reviews, QBR prep)

Early member of an enterprise or GTM data function

This is a full-time role based in our Foster City, CA office with an in-office requirement of Monday, Wednesday, and Friday.

Full-Time Employee Benefits Include:

💰 Competitive Salary & Equity

💹 401(k) Program with a 4% match (US Only)

⚕️ Health, Dental, Vision and Life Insurance

🩼 Short Term and Long Term Disability

🚼 Paid Parental, Medical, Caregiver Leave

🏝 Flexible Time Off (FTO) + Holidays

🚗 Commuter Benefits (In-Office & US Only)

📱 Monthly Wellness Stipend

🧑‍💻 Autonomous Work Environment

🖥 In Office Set-Up Reimbursement (In-Office Only)

🚀 Quarterly Team Gatherings

☕ In Office Amenities (In-Office Only)

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Operating Principles

Reasons not to work at Replit

To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We 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 backgrounds.