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:

1. 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.
2. 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, and ML to guide and pressure-test the team's work
• Clear point of view on how AI is reshaping data science and analytics, and experience leading teams through that shift
• Able to use GitLab, thrive in an all-remote, asynchronous company, model and coach 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 with usage-based or consumption pricing models
• Experience merging or restructuring data science and analytics teams
• Thought leadership in data science or analytics, internally and externally
• Advanced degree in a quantitative field

About the Team

Data Science at GitLab brings Data Science and Analytics together into one function. Our Data Scientists are business partners first, pairing strong business judgment with analytical rigor and statistical expertise. We help leaders decide where to invest, 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 routine reporting becomes self-serve, the team focuses on the hard problems: experimentation, causal inference, production models, and recommendations leaders act on.