What you’ll do
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
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 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
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, helping leaders make informed decisions and teams understand how their work contributes to GitLab’s growth.
The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role’s base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary.
United States Salary Range
$86,400—$176,400 USD
How GitLab Supports Full-Time Employees
Benefits to support your health, finances, and well-being
Flexible Paid Time Off
Team Member Resource Groups
Equity Compensation & Employee Stock Purchase Plan
Growth and Development Fund
Parental Leave
Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you’re excited about this role, please apply and allow our recruiters to assess your application.
Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.
Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.
GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know during the recruiting process.
