Director, Data Science

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 Director, Data Science, you'll lead GitLab's Product, Sales, and Finance Data Science teams and report to the VP of Data & Insights. You have two mandates:

• Make Data Science a driver of growth. Connect the team's work to GitLab's biggest priorities: supporting product innovation, making sure what we launch is improved and scaled with data, improving product adoption through our GTM motions, and growing the consumption business. Make sure the work changes decisions at the executive level.

• Set the bar for great data science. Define what makes a great Data Scientist at GitLab, from business partnership to technical rigor, and develop managers who build teams where everyone shows up to that standard.

You lead through your managers with an overall team size of 12 including data scientists and analysts, ensuring direction with the team across the data organization, and staying close enough to the work to pressure-test the analyses and models that matter most.

What you’ll do

• Build trusted relationships with executives across Product, Finance, and GTM, and act as their thought partner, shaping the questions the business should be asking, not just answering them

• Own investment and prioritization across teams: decide where Data Science spends its time, and make trade-offs visible to senior leaders

• Lead high-impact, cross-functional programs, such as driving new product adoption and growing our consumption business, from strategy through measurable business impact

• Coach and mentor managers and senior ICs to grow as leaders, building succession and a bench of future leaders

• Hold a high talent bar, and make sure every teammate grows into a trusted thought partner in their area

• Set org-wide standards for metric definition, causal inference, and predictive modeling, and the role of AI in each

• Build a high-trust culture of urgency, accountability, and open communication, and act on engagement and feedback across teams

• Partner with Analytics Engineering, Data Engineering, and other data teams across the company to solve data opportunities together, aligned on a shared data and AI roadmap

What you’ll bring

• 12+ years in data science, product analytics, or a related field, including 6+ years leading teams and 3+ years managing managers in B2B SaaS or tech

• Track record of building and scaling a data science or analytics organization that executives treat as a strategic partner

• Strong business acumen across product and go-to-market, with experience connecting data work to revenue and growth outcomes

• Executive presence: communicates complex findings and trade-offs clearly to the C-suite, and coaches managers to do the same

• Deep expertise in statistics, experimentation, and causal inference, with enough technical depth in SQL, Python, an