Data Scientist, Enterprise Analytics

GitLab; United States; A distancia; USD 86.000 - 176.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health benefits

Flexible PTO

ERGs

Equity compensation

Employee stock purchase plan

Growth and development fund

Parental leave

Descripción de la vacante

GitLab Inc. is seeking a Data Scientist for the Enterprise Analytics team to partner with Sales, Revenue Operations, and Finance. You will apply statistics, experiments, and ML to forecast outcomes, explain drivers of activation, conversion, and retention, and size opportunities. You’ll support company-wide reporting on ARR and first orders.

The role emphasizes production-quality Python/SQL, MLOps, and collaboration across GTM teams, with a fully remote work model and US salary range.

Formación

• 3–5 years in GTM analytics or data science or a similar technical role.
• Translate ambiguous business problems into clear, measurable questions.
• Experience with experimentation and causal inference (A/B testing, quasi-experiments).
• Deep SQL in cloud data warehouses (Snowflake) and BI tools (Looker/Tableau).
• Python for statistical modeling and production data science (pandas, NumPy, scikit-learn).
• Hands-on experience building agentic AI workflows and related tooling (MLflow, Claude/OpenCode).
• Experience in a B2C company with Consumption, ARR, CRR, churn metrics.
• Self-motivated, well organized, and able to thrive in a remote setting.

Responsabilidades

• Embed with business teams to become a trusted partner for insight and decisions.
• Perform exploratory data analysis to uncover trends and drivers of performance.
• Build and productionize ML models with MLOps practices for reliability.
• Design and evaluate growth experiments on onboarding, pricing, and upgrades.
• Develop agentic workflows for analysis and automation with traceability.
• Contribute to GitLab’s Semantic Layer for self-serve metrics.
• Write production-grade Python and SQL in a high-scale environment.

Conocimientos

GTM analytics

Data storytelling

Experimentation & causal inference

SQL (Snowflake)

Python for data science

Machine learning modeling

AI agent workflows

B2C metrics experience

Remote work capability

Herramientas

Looker

Tableau

Hex

MLflow

Jupyter

Descripción del empleo

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 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.

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.

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.

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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.