Strategic Overview
Senior Manager, Data Science at GitLab is defined by the responsibilities and candidate requirements in the employer's listing.
The published role emphasizes work such as Set a long-range vision and roadmap for Product Data Science, and show the team how its goals connect to company strategy Own prioritization and trade-offs across product areas, and adapt the team as priorities shift.
The role is listed for United States with remote working arrangement, giving candidates the practical context needed to assess fit before applying.
Key information at a glance
About This Role
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…
* 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.
Key Responsibilities & Objectives
- Set a long-range vision and roadmap for Product Data Science, and show the team how its goals connect to company strategy
- Own prioritization and trade-offs across product areas, and adapt the team as priorities shift
- Be the go-to partner for Product and Engineering leaders on complex and urgent questions, choosing the right approach: a deep dive, an experiment, or a production model
- Raise risks and resource gaps early, and bring senior leaders clear options to drive timely decisions
Required Qualifications & Candidate Blueprint
- 10+ years in data science, product analytics, or a related field, including 3+ years managing data science or analytics teams in B2B SaaS or tech
- Strong product sense and a track record as a trusted partner to Product and Engineering leaders
- Exceptional communicator who makes complex problems and findings clear to any audience, from Product leaders to executives, and coaches others to do the same
Preferred Qualifications
- Experience with developer tools, DevSecOps, or product-led growth products
- Experience measuring adoption and quality of AI-powered product features
- Thought leadership in data science or analytics, internally and externally
Salary & Benefits
Published compensation: $152,800 – $259,200 USD
- Set a long-range vision and roadmap for Product Data Science, and show the team how its goals connect to company strategy
- Benefits to support your health, finances, and well-being
- Flexible Paid Time Off
- Equity Compensation & Employee Stock Purchase Plan
- Parental Leave
Salary Benchmark
This role’s published compensation is compared with active employer-disclosed ranges for the same role at the Senior level, in the same currency and pay period.
Benchmark uses active employer job postings in our database, excludes this listing, and matches the currency and pay period. It is not a third-party salary estimate; currencies are not converted.
Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
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 Senior Manager, Data Science, you'll lead Product Data Science: a team of 6 Data Scientists within GitLab's data organization, reporting to the Director of Data Science. You have two mandates:
- Drive product impact now. Set the analytical agenda for your product areas and make sure the team's work changes decisions.
- Build the merged Data Science function. Define excellence for a Data Scientist who blends analytics and data science, and grow the team to that bar.
You lead mainly through others, and step in hands-on when it matters: reviewing a model, pressure-testing an analysis, or writing the query yourself.
What you’ll do
- Set a long-range vision and roadmap for Product Data Science, and show the team how its goals connect to company strategy
- Own prioritization and trade-offs across product areas, and adapt the team as priorities shift
- Be the go-to partner for Product and Engineering leaders on complex and urgent questions, choosing the right approach: a deep dive, an experiment, or a production model
- Raise risks and resource gaps early, and bring senior leaders clear options to drive timely decisions
- Hire, coach, and retain a high-performing team: build career paths, act on team feedback, and make tough talent decisions when needed
- Build a high-trust culture of urgency and accountability, where the team communicates openly, shares progress on a predictable cadence, and delivers quality results on time
- Set standards for experimentation, causal inference, metric definition, and production modeling
What you’ll bring
- 10+ years in data science, product analytics, or a related field, including 3+ years managing data science or analytics teams in B2B SaaS or tech
- Strong product sense and a track record as a trusted partner to Product and Engineering leaders
- Exceptional communicator who makes complex problems and findings clear to any audience, from Product leaders to executives, and coaches others to do the same
- Thrives in ambiguity: structures undefined problems, gathers the right information, and makes sound decisions on high-risk, cross-team issues
- Courage to challenge: offers dissenting views, asks tough questions, and fosters inclusive debate
- Deep expertise in statistics and experimentation, including hands-on use of quasi-experimental methods (difference-in-differences, synthetic control, propensity score matching)
- Expert SQL (Snowflake preferred) and strong Python, with experience taking ML models to production
- Fluent with AI tools, with sound judgment on when AI output can be trusted
- Able to use GitLab, thrive in an all-remote, asynchronous company, model GitLab's remote working competencies, and work in line with GitLab's values
Nice to Have
- Experience with developer tools, DevSecOps, or product-led growth products
- Experience measuring adoption and quality of AI-powered product features
- Thought leadership in data science or analytics, internally and externally
- Advanced degree in a quantitative field
About the Team
Product Data Science brings Data Science and Analytics together into one function. Our Data Scientists are business partners first, pairing strong product judgment with deep skill in SQL, Python, statistics, and machine learning. We help Product and Engineering decide what to build, measure what worked, and understand how customers get value from GitLab.
The opportunity is growing fast. AI is driving growth in GitLab's consumption business, creating new ways for data to drive growth. As AI takes over routine reporting, the team focuses on the hard problems: experimentation, causal inference, production models, and recommendations leaders act on.
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.
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.
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This opening may suit candidates whose experience matches the role-specific capabilities named in the listing. Its strongest career signal is the scope of the work itself — particularly Set a long-range vision and roadmap for Product Data Science, and show the team how its goals connect to company strategy — rather than a generic title alone.
Resume & Application Strategy
For the application, mirror the employer's language where it truthfully matches your background: foreground the role-specific capabilities named in the listing, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.
We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.