Head of Data Science, Cleo AI

• 7+ years in product data science / analytics with a track record of improving user and commercial outcomes
• Proven experience in product analytics (e.g., onboarding/activation, conversion funnels, retention) in consumer products
• Experience leading a high‑performing team (at least 2–3 data scientists/analysts; managing a mix of senior and mid-level is a plus)
• Strong influence and communication: You can tell a clear, structured story with data and land insights at the right time to shape decisions. You’ve set/defended targets with senior stakeholders and created broad alignment
• Strong experimentation skills: you’ve designed and interpreted large-scale A/B tests, understand common pitfalls and best practices, and are comfortable guiding others
• Strong SQL and Python skills, and comfort working directly with modern analytics tools and workflows
• You can work end‑to‑end: from defining tracking and metric definitions through to analysis and decision support

Desirable

• We’re hiring a Head of Data Science to own analytics and experimentation for a product pillar at Cleo
• You’ll lead a team of 4–5 product data scientists embedded in cross‑functional squads and partner closely with Product, Engineering, Design, and ML leadership to shape what we build. This is a player–coach role: you’ll set the bar for analytics craft across the pillar and stay hands‑on with high‑impact analysis when decisions need to move fast
• You’ll be the analytics leader for the AI Financial Assistant pillar, with a direct line of sight to engagement, revenue, and product roadmap. Your team’s work will directly influence how we acquire, activate, convert, and retain users, improve payback and LTV, and scale Cleo to millions more people sustainably
• Pillar leadership: roadmaps and decision‑making
• Partner with pillar leaders to build a discovery and experimentation roadmap across acquisition, onboarding, conversion, and retention
• Define success metrics, metric trees, and guardrails so squads make consistent, data‑driven decisions
• Support and challenge teams during OKR planning, ensuring initiatives map to clear metrics, realistic targets, and understood trade‑offs
• Provide visibility to senior leadership on pillar performance: what’s driving key metric movement, where the biggest opportunities are, and what we’ll do next
• Team leadership: coaching and high performance
• Line-manage product data scientists, supporting performance, development, and career growth
• Ensure analysts are focused on the highest‑impact work and delivering against pillar priorities
• Coach on stakeholder management, structured problem solving, storytelling, experimentation, and commercial thinking
• Raise the bar for analytics craft through feedback, pairing, templates/playbooks, and lightweight standards that improve quality and speed
• Hands‑on analytics and experimentation
• Personally deliver high‑impact analysis that shapes pillar roadmaps and investment decisions
• Deep dive into key metrics to understand trends, root causes, and differences across products and segments
• Own the experimentation program in your pillar: Ensure tests are robust, interpretable, and aligned to strategy. Maintain a test roadmap, prevent collisions, and resolve cross-team conflicts
• Turn insights into shipped change: align owners and timelines, remove blockers, and close the loop on outcomes
• Data foundations
• Champion high‑quality instrumentation, clear metric definitions, and data hygiene
• Work with Analytics Engineering to prioritise modelling needs and ensure the right datasets and definitions exist for your pillar
• Build and oversee dashboards/tools that help squads and leaders act on metrics without always relying on analysts

• Recruiter Interview (30 mins)
• Leadership & Communication Interview (60 mins)
• Technical Interview (60 mins)
• Whiteboard Interview (60 mins)
• Final Interview with the Hiring Manager (45 mins)