Data Scientist, Enterprise Analytics

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100 trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: 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. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. *Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role 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. 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 of growth. Build and own data models in Python and SQL for propensity, churn, expansion, retention, and other key GTM motions. Design and analyze experiments (A/B tests, quasi-experiments) to measure impact and guide investment decisions. Productionize models, dashboards, and reusable analysis with robust MLOps practices, leveraging dbt, data validation, and Airflow. Partner with Analytics Engineering and Data Engineering to ensure the data we need is reliable, well-modeled, and accessible. Explore and build agentic analysis workflows (e.g., natural language queries, autonomous insight generation) to scale our analytical capacity and delight stakeholders. Present findings in a clear, concise, and compelling narrative format to stakeholders at all levels — including executives. What you’ll bring Experience working with large, complex datasets (preferably in SaaS, ideally in a B2B context) Experience in founding or scaling an Enterprise Analytics practice or working in a mature B2B analytics function Deep expertise in causal inference, statistics, experimental design, and model evaluation; familiarity with ML algorithms and the full data science lifecycle Strong proficiency in SQL and Python for data manipulation, statistical analysis, and model building, with Git-based version control and production-quality code practices Hands-on experience with modern data tools; dbt and Airflow experience is strongly preferred Knowledge of statistics and machine learning methods including regression, classification, clustering, time-series analysis, etc. Expertise in data visualization and dashboarding, with Looker Studio, Tableau, or similar tools, and a track record of creating intuitive, actionable dashboards Experience with experimentation platforms and A/B testing, including designing, running, and analyzing experiments and digital experiments Excellent communication and presentation skills, with the ability to translate complex analytical concepts and results for non-technical stakeholders Strong problem-solving skills and a detail-oriented mindset, with experience in breaking down complex business problems into solvable data science tasks Ability to work effectively in a remote, distributed, and async-first environment, with excellent time management and self-direction; experience working with remote teams across different time zones How GitLab will support you Benefits to support your health, finances, and well-being All remote, asynchronous work environment Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and development budget Parental leave Home office support Hiring process See the hiring process at GitLab. Compensation The base salary range for this role’s listed level is currently $86,400 – $176,400 for residents of the United States only. 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, and alignment with market data. See more information on our benefits and equity . GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.