Senior Data Science Manager, 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 Senior Data Science Manager, Product Analytics to own analytics and experimentation for the Chat 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 Win on Data (aka User & Financial Insights), with 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)
• Whiteboard Interview (60 mins)
• Technical Interview (60 mins)
• Leadership & Communication Interview (60 mins)
• Final Interview with the Hiring Manager (45 mins)