Data Scientist, Enterprise Analytics - GitLab

About GitLab

GitLab is the intelligent orchestration platform for DevSecOps, enabling organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and over 50% of the Fortune 100 trust GitLab to ship better, more secure software faster.

We embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued.

Overview of the Role

As a Data Scientist on the Enterprise Analytics team, you’ll be a trusted analytical partner to Sales, Revenue Operations, and Finance, helping define how GitLab measures go-to-market performance and where to invest for growth.

This is both a strategic and hands-on role. You’ll apply statistical, experimentation, and machine learning methods to forecast outcomes, explain drivers behind 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.

What You'll Do

• Embed 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 key drivers of business performance, communicating impact clearly to non-technical audiences.
• Build and productionize machine learning models (propensity, PQL scoring, churn, expansion), managing training, validation, testing, feature engineering, and MLOps practices (CI/CD for ML, model registries, automated retraining, and monitoring).
• Design, run, and evaluate growth experiments (A/B tests and quasi-experiments) on onboarding, pricing, packaging, and upgrade flows to measure impact on activation, conversion, and retention.
• Build and operationalize agentic workflows for analysis, modeling, and pipeline automation, with evaluation and monitoring to ensure agent-generated insights are accurate, traceable, and trustworthy.
• Contribute to GitLab’s Semantic Layer to enable stakeholders and agents to self-serve metrics using trusted and tested data sources.
• Write production-grade Python and SQL meeting internal standards for style, maintainability, and performance in a high-scale database environment.

What You'll Bring

• 3–5 years of professional experience in GTM analytics, data science, or a similar technical role.
• Demonstrated ability to translate ambiguous business problems into 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 statistical/ML techniques (regression, classification, clustering, survival analysis).
• Deep, hands-on SQL proficiency in cloud data warehouses (such as Snowflake), working with product usage and event data, alongside BI tools (Looker, Tableau, Hex, etc.).
• Strong experience using Python for statistical modeling, machine learning, and production data science (pandas, NumPy, scikit-learn, Jupyter, LightGBM, XGBoost).
• Hands-on experience building agentic workflows with AI coding agents (e.g., Claude Code, OpenCode), connecting agents to context, tools, and platforms like MLflow, and evaluating outputs for accuracy.
• Experience working in a B2B/B2C company with GTM motions and SaaS metrics (Consumption, CRR, ARR, Churn, Contraction).
• Self-motivated and self-managing, with the ability to thrive in a fully remote organization.

About the Team

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 to help leaders make informed decisions.