Data Scientist, Enterprise Analytics

GitLab

11h•$86,400 - $176,400•Remote

About The Position

As a Data Scientist on GitLab’s Enterprise Analytics team, you will partner with Sales, Revenue Operations, and Finance to shape how the company measures go-to-market performance and prioritizes growth investments. This is a strategic, hands-on role applying statistical, experimentation, and machine learning methods to forecast outcomes, understand drivers of activation, conversion, expansion, and retention, and size business opportunities. You will also support company-level reporting and GTM performance views used by executives, including consumption, ARR, and first orders. The Enterprise Analytics team builds trusted data products and insights for company-wide sales, revenue, and finance reporting. It works across the business on metrics such as First Orders, pipeline, CRR, and ARR, helping leaders make informed decisions and teams understand their contribution to GitLab’s growth. The role is remote.

Requirements

3–5 years of professional experience in GTM analytics, data science, or a similar technical role.
Ability to translate ambiguous business problems into clear, measurable questions and explain 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 work with product usage and event data.
Experience with business intelligence tools such as Looker, Tableau, or Hex, or similar tools.
Experience using Python for statistical modeling, machine learning, and production data science, including 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; connecting agents to relevant context, tools, and platforms such as MLflow; and evaluating output for accuracy and reliability.
Experience working at a B2C company 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 thrive in a fully remote organization.

Responsibilities

Partner with business teams across go-to-market to generate insights and support strategic decision-making.
Conduct exploratory data analysis to identify behavioral trends, long-term patterns, and drivers of business performance, and communicate findings clearly to non-technical audiences.
Build and productionize machine learning models, including propensity, PQL scoring, churn, and expansion models; handle training, validation, testing, and feature engineering.
Use MLOps practices and tooling, including CI/CD for ML pipelines, model registries, automated retraining, and monitoring, to move work reliably from notebooks to production.
Design, run, and evaluate A/B tests and quasi-experiments involving onboarding, pricing and packaging, and upgrade flows; measure effects on activation, conversion, and retention.
Build and operationalize agentic workflows for analysis, modeling, and pipeline automation, with evaluation and monitoring to ensure generated insights are accurate, traceable, and trustworthy.
Contribute to GitLab’s Semantic Layer so stakeholders and agents can self-serve metrics using trusted and tested sources.
Write production-grade Python and SQL that meet internal standards for style, maintainability, and performance in a high-scale database environment.
Maintain and advocate for code standards through code review.