Director of Data Science, Cleo AI

• 8+ years in data analytics, data science, BI engineering, analytics engineering ideally in a high-growth, product-led company
• Proven management experience of at least 4+ ICs
• Deep experimentation expertise that goes well beyond "is it stat sig" → you've fixed structural problems in a testing programme: bucketing, conflicts/collisions, sizing, priors, governance, and how to measure the health of the programme itself
• A builder's instinct: bias for automation, ownership, and shipping
• Ability to set standards and an operating model that teams actually adopt — intake, quality bars, ways of working that measurably improve delivery beyond a single team
• Commercial sense - can translate product/user metrics into revenue, profitability and LTV, and hold your own with Product Directors and Commercial stakeholders
• You're a systems thinker; comfort operating in a mature, complex product environment with lots of interacting parts — for example (but not necessarily required to be these teams) payments systems, credit risk, ML models feeding user experience
• Exec-ready storytelling - you can synthesise across pillars and land insight at the right moment to shift Director/SLT-level prioritisation; you've set and defended targets with senior stakeholders
• Player-coach instinct - you create leverage through delegation, templates and self-serve, but you'll happily go hands-on on the highest-leverage or most ambiguous problem when it's the right call
• Comfortable using AI tools (Claude, Cursor) as leverage - with the judgement to know when to trust output and when to verify
• Interest in fintech and Cleo's mission to make money easier and more human

Desirable

• We're hiring a Director of Data Science to own product analytics across Cleo's Financial Services Product pillars — EWA, Credit Products, Payments and Fraud. This is a manager-of-managers role: you'd lead the analytics leadership layer (L4/L5 managers and senior ICs) rather than sit in a single squad
• The job, in a nutshell, is to make sure product analytics works as well as it possibly can across these pillars - not just answering questions accurately, but shaping the decisions that get made in the first place. That means three things pulling in the same direction: a strong analytics leadership bench, an experimentation and measurement programme that's genuinely trustworthy at scale, and an operating model that means squads and leaders can act on metrics without an analyst holding their hand
• You'll be the senior analytics partner to Product, Engineering, Commercial and ML leadership for these pillars - the person who can synthesise across all of it and change what a Product Director or the SLT decides to prioritise. And because these products drive the majority of company revenue, this role has direct line of sight to engagement, retention, and the commercial numbers that matter most
• Own the analytics strategy and operating model across Cleo's Financial Services Product pillars -coherent measurement, experimentation and insight cadence that maps to company priorities over multiple terms
• Lead and develop the analytics leadership layer — L4/L5 managers and senior ICs — building a strong bench, clear growth plans, and succession so the function runs well without any single person as a failure point
• Drive the experimentation programme as a product: fix the structural issues (single source of truth, standard bucketing, automated conflict detection, sizing and priors) and raise the bar on rigour and interpretation across squads
• Improve observability of live user flows and metric movements — partner with Engineering on the event framework and product-health reporting so we can answer "why did this move?", not just "what moved?"
• Champion responsible use of agentic AI as a force multiplier for analytics — speeding up common investigations, training, and basic analysis with sensible guardrails
• Partner with Product, Credit, Engineering, Commercial and ML leadership to shape roadmaps, planning and trade-offs with evidence — and give senior leadership clear visibility on what's driving key metrics and where the biggest opportunities are
• Set and enforce company-level standards for metrics, experimentation and reporting, driving consistency across pillars and reducing duplicated, siloed work