Head of Data Science and Analytics
at Lolanext
Amsterdam, Netherlands; Belgrade, Serbia; Berlin, Germany; Limassol, Cyprus; London, United Kingdom; Madrid, Spain; Munich, Germany; Paphos, Cyprus; Prague, Czech Republic; Remote, Germany; Warsaw, Poland; Yerevan, Armenia, hybrid, executive, full time
posted 02 Nov 2026
- Data Science
- Cohort analysis
- Data Engineering
- Segmentation
- Data modeling
- A/B testing
- Machine Learning
- Analytics
About the role
Jetbrains · Amsterdam, Netherlands; Belgrade, Serbia; Berlin, Germany; Limassol, Cyprus; London, United Kingdom; Madrid, Spain; Munich, Germany; Paphos, Cyprus; Prague, Czech Republic; Remote, Germany; Warsaw, Poland; Yerevan, Armenia · posted 2026-11-02 · English-friendly ✓
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You’ve built data teams from the ground up and know how to turn raw information into better product decisions. You care deeply about how AI is changing software development, and you want to shape the data foundations that make it happen.
What you would do
JetBrains Air is an AI-native software development platform that connects developer workflows, collaboration, and control into a single system. Because this is a major strategic focus for us, we need a strong data function to help guide it. As Head of Data Science and Analytics for JetBrains Air, you’ll lead our data science, analytics, and data engineering capabilities for both the product and go-to-market domains.
By building a high-performing team, you’ll help us use data to navigate product direction, optimize product and go-to-market decisions, and accelerate growth. You’ll own the data strategy for JetBrains AI, partnering closely with product, engineering, and leadership teams to ensure our data infrastructure scales smoothly alongside our ambitions.
* Build and lead a cohesive team of data scientists, analysts, and data engineers. * Help teams adopt a rigorous experimentation culture, improving how we test and measure product changes. * Uncover insights across the customer journey to improve acquisition, retention, and monetization. * Establish scalable analytics foundations, including clear metric frameworks, data models, and self-service reporting. * Partner with engineering teams to design robust, compliant data infrastructure and pipelines. * Guide the development of machine learning capabilities that enhance marketing acquisition, product personalization, and operational efficiency. * Translate complex data signals into clear, actionable recommendations for leadership. * Define the business unit's data strategy to support product and engineering priorities.
Read from lolanext.com, last seen 4d ago.