[All remote jobs](https://jobicy.com/jobs.md)[![GitLab logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2020/12/WRILS-201207055737-109952.jpg)](https://jobicy.com/company/gitlab.md)[GitLab](https://jobicy.com/company/gitlab.md)

# Data Scientist, Enterprise Analytics

AI Summary GitLab is hiring a Data Scientist to support Enterprise Analytics across Sales, Revenue Operations, and Finance. The role combines exploratory analysis, forecasting, experimentation, causal inference, and production machine learning for go-to-market metrics such as ARR, churn, activation, conversion, and retention. The successful candidate will build reliable Python and SQL data products, productionize ML pipelines, and develop monitored agentic workflows. This is a strategic, stakeholder-facing remote role for a self-directed data scientist with 3–5 years of relevant analytics or data science experience.[Apply for this job](#job-application)[View company](https://jobicy.com/company/gitlab.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md), [Canada](https://jobicy.com/job-region/canada.md)SalaryUSD 86,400–176,400 / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished12 Nov 2026Apply before12 Dec 2026Listing views31Application actions3Application toolkit

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Opportunity details

## About this role.

[GitLab](https://jobicy.com/company/gitlab.md) 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](https://www.youtube.com/watch?v=OuZIb5zszQI) 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.

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](https://about.gitlab.com/handbook/total-rewards/benefits/general-and-entity-benefits/) and [equity](https://about.gitlab.com/handbook/stock-options/). 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](https://about.gitlab.com/handbook/total-rewards/benefits/general-and-entity-benefits/)
* [Flexible Paid Time Off](https://handbook.gitlab.com/handbook/people-group/time-off-and-absence/time-off-types/#flexible-paid-time-off-pto)
* [Team Member Resource Groups](https://handbook.gitlab.com/handbook/company/culture/inclusion/tmrg-tmag/)
* [Equity Compensation & Employee Stock Purchase Plan](https://handbook.gitlab.com/handbook/total-rewards/stock-options/)
* [Growth and Development Fund](https://handbook.gitlab.com/handbook/people-group/learning-and-development/growth-and-development/)
* [Parental Leave](https://about.gitlab.com/handbook/total-rewards/benefits/general-and-entity-benefits/#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](https://about.gitlab.com/company/culture/inclusion/#examples-of-select-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.](https://handbook.gitlab.com/handbook/hiring/candidate-faq/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](https://about.gitlab.com/handbook/people-policies/inc-usa/#equal-employment-opportunity-policy) and [EEO is the Law](https://about.gitlab.com/handbook/labor-and-employment-notices/#eeoc-us-equal-employment-opportunity-commission-notices). If you have a disability or special need that requires [accommodation](https://about.gitlab.com/handbook/people-policies/inc-usa/#reasonable-accommodation), please let us know during the [recruiting process](https://about.gitlab.com/handbook/hiring/interviewing/#adjustments-to-our-interview-process).

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[Apply now >](https://jobicy.com/jobs/161680-data-scientist-enterprise-analytics.md)

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![Upload CV](data:image/svg+xml;base64,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)

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## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

4/5EasyHard

### Pace & Pressure

4/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

5/5IndependentCollaborative

AI insightThe position requires strong technical breadth across experimentation, causal inference, machine learning, MLOps, analytics engineering, and AI-agent evaluation. It also demands translating ambiguous revenue and product questions into executive-ready business decisions.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate$131,400US market range (per year)$110k–$180k0$198.00k

AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a US base salary range of $86,400–$176,400 USD. The annual cadence is inferred from the stated base-salary context, amount scale, and standard US full-time professional compensation conventions. A reasonable estimated US market range for this data science role is $110,000–$180,000 per year.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Statistical Modeling](https://jobicy.com/jobs?search_keywords=Statistical%20Modeling.md)[Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[A/B Testing](https://jobicy.com/jobs?search_keywords=AB%20Testing.md)[Causal Inference](https://jobicy.com/jobs?search_keywords=Causal%20Inference.md)[Forecasting](https://jobicy.com/jobs?search_keywords=Forecasting.md)[Feature Engineering](https://jobicy.com/jobs?search_keywords=Feature%20Engineering.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[Looker](https://jobicy.com/jobs?search_keywords=Looker.md)[scikit-learn](https://jobicy.com/jobs?search_keywords=scikit-learn.md)[XGBoost](https://jobicy.com/jobs?search_keywords=XGBoost.md)[Data Visualization](https://jobicy.com/jobs?search_keywords=Data%20Visualization.md)[Stakeholder Communication](https://jobicy.com/jobs?search_keywords=Stakeholder%20Communication.md)

Sample interview questionsHow would you evaluate whether a new onboarding flow improves activation?I would define a primary activation metric and guardrail metrics, then run a randomized A/B test with an upfront power analysis. I would validate assignment quality, measure the treatment effect with confidence intervals, assess segment-level heterogeneity, and communicate both the business impact and uncertainty.

Describe how you would build a churn propensity model for an enterprise product.

I would first define the churn horizon and unit of analysis, then create leakage-safe features from product usage, account, support, and commercial data. I would compare interpretable baselines with models such as LightGBM, validate using time-based splits, calibrate probabilities, and monitor drift, performance, and intervention outcomes after deployment.

How do you translate an ambiguous executive question into an analytical plan?

I begin by clarifying the decision to be made, the affected population, the success metric, time horizon, and available actions. I document assumptions, identify data limitations and likely confounders, propose an appropriately simple method, and align with stakeholders before conducting the analysis.

What practices would you use to productionize a machine learning workflow?

I would version code, data definitions, features, and model artifacts; add automated tests and reproducible training pipelines; and use CI/CD and a model registry for controlled releases. I would also establish monitoring for data quality, drift, model performance, latency, and business outcomes, with clear rollback procedures.

How would you validate insights generated by an AI agent used in an analytics workflow?

I would constrain the agent to governed semantic-layer metrics and approved tools, require traceable source queries, and evaluate outputs against a curated benchmark set. I would implement human review for high-impact decisions, monitor error patterns, and continuously improve prompts, context, permissions, and automated checks.

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