Director of Data Science, Financial Services

Most money apps talk o talks back.

We're not building another finance app. We're building the world's first AI financial assistant, one that actually understands your money and makes you better at it, and we're changing the world's relationship with money in the process. For everyone, whatever their background or balance.

The proof: profitable, fast-growing, a unicorn with over $300M in ARR, and millions of people who now feel differently about their money.

We love original thinkers who challenge the status quo. We move fast, tell the truth, and there's nowhere to hide from good work here. If that excites you more than it scares you, you'll fit right in.

Follow us on Linked In for new features and the occasional roast.

Here at Cleo, we have ~70 Data Scientists (Product Analysts) working embedded into our Pillars. They stay connected and share knowledge consistently through monthly meetings and slack channels to scale approaches and ensure everyone learns in the same way. They thrive through clear communication with their squad and stakeholders to influence product or company decisions, being that bridge between the non-tech and technical sides of the team.

We have deeply structured data and a great experimentation culture using Stat Sig for all analysts to dive into!

We're hiring a senior analytics leader to own the analytics strategy and operating model across Cleo's Financial Services Product pillars
. You'll lead and develop the analytics leadership layer itself - L4/L5 managers and senior ICs - setting company-level standards for metrics, experimentation, and reporting across pillars.

• Own the analytics strategy and operating model across Financial Services Product pillars - coherent measurement, experimentation, and insight cadence mapped to company priorities
• Lead and develop the analytics leadership layer (L4/L5 managers and senior ICs), building a strong bench, growth plans, and succession so the function doesn't rely on any single person
• Drive the experimentation programme as a product - fixing structural issues like single source of truth, standard bucketing, automated conflict detection, sizing and priors - and raise the bar on rigour across squads
• Improve observability of live user flows and metric movements, partnering with Engineering on the event framework and product-health reporting
• Champion responsible use of agentic AI as a force multiplier for analytics, with sensible guardrails
• Partner with Product, Credit, Engineering, Commercial, and ML leadership to shape roadmaps and trade-offs with evidence, giving senior leadership clear visibility on what's driving key metrics
• Set and enforce company-level standards for metrics, experimentation, and reporting, reducing duplicated, siloed work across pillars

• 8+ years in data analytics, data science, BI engineering, or analytics engineering, ideally in a high-growth, product-led company
• Proven management experience of at least 4+ ICs
• Deep experimentation expertise beyond stat-sig testing - fixing structural problems in a testing programme: bucketing, conflicts/collisions, sizing, priors, governance
• A builder's instinct: bias for automation, ownership, and shipping
• Ability to set standards and an operating model that teams actually adopt, measurably improving 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
• Systems thinker, comfortable in a mature, complex product environment with interacting parts (e.g. payments, credit risk, ML models feeding user experience)
• Exec-ready storytelling - can synthesise across pillars, land insight at the right moment, and set and defend targets with senior stakeholders
• Player-coach instinct - creates leverage through delegation, templates, and self-serve, but goes hands-on on the highest-leverage or most ambiguous problems when needed
• 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
• Nice to have: causal inference depth (CUPED, Bayesian methods, PSM, uplift modelling) and the judgement to know when it's worth the effort
• Nice to have: genuine enthusiasm for agentic AI as a force multiplier in analytics workflows
• Nice to have: credit-risk analytics exposure - arrears, defaults, how ML risk models interact with product…