Data Science Manager; Finance Analytics
Job in
Greater London, London, Greater London, W1B, England, UK
Listed on 2026-06-19
Listing for:
Cleo AI
Full Time
position Listed on 2026-06-19
Job specializations:
-
IT/Tech
Data Science Manager, Data Analyst, Business Systems/ Tech Analyst
Job Description & How to Apply Below
Location: Greater London
Requirements
- 7+ years in data analytics, data science, BI engineering, analytics engineering, or hybrid data/finance roles - including 2+ years line managing data scientists or analysts, with experience handling performance management
- Track record of partnering senior business stakeholders - ideally across Finance, Strategic Finance, Capital Markets, or Operations - and shaping decisions at exec level
- Familiarity and some working experience with forecasting and predictive models; enough to interpret outputs, pressure-test assumptions, and direct ICs working in this space
- Strong commercial instincts: deep grasp of financial and operational metrics, and quick to pick up the levers in a new domain
- Comfortable being the most senior voice in the room on data, and pushing back constructively when the analysis doesn't support the ask
- Expert SQL, hands-on with BI tools (e.g. Looker, Mode, Tableau) and dbt
- A builder's instinct: bias for automation, ownership, and shipping - and the leadership instinct to scale that across a team through delegation, templates, and systems rather than doing more yourself
- 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) Exposure to capital markets, debt facility reporting, or lender-side analytics
- (Desirable) Prior experience automating or supporting investor-facing reporting or board materials
- (Desirable) Experience working in environments with a blend of consumer tech and financial services (e.g. AI, lending, credit, payments)
- (Desirable) Extensive hands-on experience building and/or monitoring forecasting or predictive models in production
- (Desirable) Python (or similar) for scripting and automation
- (Desirable) Workflow orchestration tooling (e.g. Airflow)
- We're hiring a Data Science Manager, Strategic Finance Analytics to lead one of the most cross-functional and high-leverage analytics functions at Cleo
- You'll manage the Strategic Finance sub-team — data scientists embedded across FP&A, Core Finance, Capital Markets, and Operations — and the senior relationships that keep these functions genuinely data-led
- This is a people management role as much as a technical one — you'll be responsible for performance, development, career growth and hiring while staying close enough to the work to have a real point of view
- The day-to-day split is roughly 70% people leadership and 30% hands-on yourself: jumping into the highest-leverage analyses, presenting at exec forums, and shaping the analytical roadmap across all four domains
- You'll partner closely with the Heads and Directors across each department, sitting at the heart of commercial decision-making orting into the Director of Commercial Analytics, this role touches some of the most consequential decisions made across the business
- Lead the Strategic Finance Analytics team — 5 data scientists across FP&A, Core Finance, Capital Markets, and Operations. Own their growth, performance, and hiring
- Set and ruthlessly prioritise the analytical roadmap across all four domains, directing the team's time to the work that moves the business most
- Be the senior analytical partner to Heads of department — shaping decisions at exec level, not just delivering numbers to them
- Stay hands-on where it matters: high-stakes analysis, sensitive work, or decisions that need to move fast
- Own the quality bar — metric definitions, documentation, peer review, and trust in the numbers across internal and external reporting
- Raise the craft of the team through feedback, pairing, and playbooks — so output scales beyond what 5 people can deliver directly
- Partner with Analytics and Data Engineering on the data foundations the team relies on
- Run team rituals and represent the team in cross-functional forums
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