Data Scientist, Enterprise Analytics

As a Data Scientist at GitLab on the Enterprise Analytics team,  you’ll be a trusted analytical partner to Sales, Revenue Operations, and Finance, helping define how we measure go-to-market performance and where we invest to grow. This is a strategic and hands-on role. You’ll apply statistical, experimentation, and machine learning methods to forecast outcomes, explain what drives activation, conversion, expansion, and retention, and size opportunities. You’ll also support the company-level reporting and GTM performance views that executives use to run the business, covering consumption, ARR, and first orders.

• Embed yourself with business teams to become a trusted partner supporting insight generation and strategic decision making across GTM.
• Perform exploratory data analysis to uncover behavioral trends, long-term patterns, and the key drivers and levers behind business performance, and communicate their impact clearly to non-technical audiences.
• Build and productionize machine learning models - such as propensity, product-qualified lead (PQL) scoring, churn, and expansion models - including training, validation, testing, and feature engineering, using MLOps practices and tooling (CI/CD for ML pipelines, model registries, automated retraining, and monitoring) to move work reliably from notebook to production
• Design, run, and evaluate growth experiments (A/B tests and quasi-experiments) on onboarding, pricing and packaging, and upgrade flows, and measure their impact on activation, conversion, and retention
• Build and operationalize agentic workflows for analysis, modeling, and pipeline automation, with evaluation and monitoring that keep agent-generated insights accurate, traceable, and trustworthy.
• Contribute to GitLab’s Semantic Layer enabling stakeholders and agents to self-serve metrics using trusts and tested sources.
• Write production-grade Python and SQL that meets internal standards for style, maintainability, and performance in a high-scale database environment; maintain and advocate for these standards through code review

• 3-5 years of professional experience in GTM analytics, data science, or a similar technical role
• Demonstrated ability to translate ambiguous business problems into clear, measurable questions and communicate technical concepts effectively to non-technical stakeholders.
• Experience with experimentation and causal inference (A/B testing, power analysis, quasi-experimental methods) and with statistical and machine learning methods such as regression, classification, clustering, and survival analysis
• Deep, hands-on understanding of SQL in cloud data warehouses (like Snowflake SQL), including working with product usage and event data, and in business intelligence tools (Looker, Tableau, Hex, or similar)
• Experience using Python for statistical modeling, machine learning, and production data science. With hands-on experience in pandas, NumPy, scikit-learn, Jupyter, and related libraries such as LightGBM and XGBoost
• Hands-on experience building agentic workflows with AI coding agents (e.g., Claude Code, OpenCode, or similar), including connecting agents to the right context, tools, and platforms such as MLflow, and evaluating their output for accuracy and reliability.
• Experience working in a B2C company working with motions and metrics, including Consumption, CRR, ARR, and Churn & Contraction
• Self-motivated and self-managing, with strong organizational skills
• Ability to use GitLab and to thrive in a fully remote organization

The Enterprise Analytics team builds the trusted data products and insights behind GitLab’s Company wide sales, revenue, and finance reporting. We work across the business on metrics including First Orders, pipeline, CRR, ARR, and other key indicators, helping leaders make informed decisions and teams understand how their work contributes to GitLab’s growth.