Applied Data Scientist

What You'll Do

• Identify high-value opportunities from product, customer, and operational data
• Evaluate ambiguous ideas quickly and determine what is feasible, useful, and worth shipping
• Identify high-value opportunities from product, customer, and operational data
• Build practical 80/20 solutions that create leverage quickly, then refine them based on traction
• Own end-to-end execution across data exploration, modeling, experimentation, backend integration, and productization
• Partner with engineering, product, design, and leadership to turn rough ideas into shipped capabilities
• Use ML, analytics, heuristics, and automation pragmatically rather than forcing a model where one is not needed
• Define success metrics, instrument outcomes, and improve solutions based on real-world usage
• Help shape how GitKraken uses AI and data to improve developer workflows, team velocity, and product experience

Our Tech Lens

• Languages: Python (for data/ML execution), alongside Go and TypeScript across our core product and backend environments
• Data & Infrastructure: Snowflake for data warehousing, AWS for cloud infrastructure, and Datadog for monitoring and observability
• AI Ecosystem & DevEx: We live and breathe developer experience. We heavily leverage and build around modern AI development tools and LLMs like Cursor, Claude Code, and Codex to accelerate execution and shape the future of workflows

What We're Looking For

• Deep experience in machine learning, applied AI, or a similarly hands-on product data role at a Senior level
• A track record of shipping data or ML-powered capabilities into real products or operational workflows
• Comfort moving from messy problem statements to practical execution without a lot of structure
• Ability to work across the stack, not just in notebooks
• Strong product judgment and a bias toward simple solutions that deliver measurable value
• Experience deciding whether a problem is best solved with ML, rules, analytics, automation, or workflow design
• Ability to balance speed and rigor, including knowing when “good enough to learn” is the right answer
• Strong communication skills and the ability to explain tradeoffs clearly to technical and non-technical partners
• Ownership mindset: you don’t wait for perfect specs, and you follow through from idea to impact
• Bonus Points: You’ve built and shipped data or ML-powered features, not just analyses; you can prototype quickly and are comfortable refining after launch; you know how to avoid getting buried in edge cases before the core value is proven; you like working in a company with a bias toward action, accountability, and high ownership; you want your work to directly influence product direction and business outcomes