Senior Manager, Data Science

Before you apply

Job source

Ats

Applying on

job-boards.greenhouse.io

Workplace

Fully remote

US state

Nationwide remote

Category

IT

Link last checked

1 hour ago

Posted

6 hours ago

Closes

No stated closing date

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About the 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 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 GitLa

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