Data Scientist, Enterprise Analytics

Details

GitLab is an intelligent orchestration platform for DevSecOps. The Enterprise Analytics team builds trusted data products and insights for company-wide sales, revenue, and finance reporting.

This strategic and hands-on role partners with Sales, Revenue Operations, and Finance to define go-to-market performance measurement and identify growth opportunities. The role applies statistical, experimentation, and machine learning methods to forecast outcomes, explain the drivers of activation, conversion, expansion, and retention, and support executive reporting across consumption, ARR, and first orders.

Responsibilities

• Partner with business teams to generate insights and support strategic decision-making across go-to-market functions.
• Perform exploratory data analysis to identify behavioral trends, long-term patterns, and key business drivers, and communicate findings to non-technical audiences.
• Build and productionize machine learning models, including propensity, product-qualified lead (PQL) scoring, churn, and expansion models.
• Apply training, validation, testing, feature engineering, and MLOps practices, including CI/CD for machine learning pipelines, model registries, automated retraining, and monitoring.
• Design, run, and evaluate growth experiments, including A/B tests and quasi-experiments, covering onboarding, pricing and packaging, and upgrade flows.
• Measure the impact of experiments on activation, conversion, and retention.
• Build and operationalize agentic workflows for analysis, modeling, and pipeline automation, with evaluation and monitoring to ensure accurate, traceable, and trustworthy insights.
• Contribute to GitLab’s Semantic Layer, enabling stakeholders and agents to self-serve metrics using trusted and tested sources.
• Write production-grade Python and SQL for high-scale database environments, following internal standards for style, maintainability, and performance.
• Maintain and advocate for technical standards through code review.

Requirements

• 3–5 years of professional experience in go-to-market analytics, data science, or a similar technical role.
• 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, including A/B testing, power analysis, and quasi-experimental methods.
• Experience with statistical and machine learning methods such as regression, classification, clustering, and survival analysis.
• Deep, hands-on SQL experience in cloud data warehouses such as Snowflake, including product usage and event data.
• Experience with business intelligence tools such as Looker, Tableau, Hex, or similar platforms.
• Experience using Python for statistical modeling, machine learning, and production data science.
• Hands-on experience with pandas, NumPy, scikit-learn, Jupyter, and related libraries such as LightGBM and XGBoost.
• Hands-on experience building agentic workflows with AI coding agents such as Claude Code, OpenCode, or similar tools.
• Experience connecting agents to appropriate context, tools, and platforms such as MLflow, and evaluating their output for accuracy and reliability.
• Experience working in a B2C company with metrics and business motions including Consumption, CRR, ARR, and Churn & Contraction.
• Strong organizational skills and the ability to work independently in a fully remote organization.
• Ability to use GitLab.

Benefits

• Benefits supporting health, finances, and well-being.
• Flexible paid time off.
• Team member resource groups.
• Equity compensation and employee stock purchase plan.
• Growth and development fund.
• Parental leave.

The role is remote and subject to location-based eligibility requirements. The listed salary range applies to United States residents and does not include bonuses, equity, or benefits.