The backdrop matters before you write a single line of a job spec. Against a subdued 2025 economic backdrop, businesses took a cautious approach to hiring, yet there were clear demand hotspots for specialist skills including AI, data, enterprise applications and cyber security, according to Computer Weekly's tech recruitment outlook for 2026. That tells you something useful: cautious overall, but hot where the skills are scarce.
Demand for data and AI talent is sharp. Demand for AI skills increased nearly 200 percent in a year, with London accounting for 80 percent of AI-related job postings and nearly two-thirds of all technology vacancies in the UK, The Register reported, citing Accenture data. For a fintech founder, that concentration is both a challenge and an opportunity: the talent exists, but you are competing for it.
The wider data and systems field is stretched too. IT business analysts, architects and systems designers, a group of 193k workers, are among the occupations in critical demand, with three out of five indicators in critical demand in 2025, according to the Occupations in demand 2025 statistics on GOV.UK. A first data scientist often sits next to these roles, so you are hiring into a tight market.
Fintech sits inside financial services, and the seniority signal there is clear. In the financial services sector, recruitment was generally for Regulatory Qualification Framework (RQF) 6+ roles, rather than RQF 3 to 5, according to the Temporary Shortage List: Stage 2 report on GOV.UK. In plain terms, employers are reaching for degree-level and above. Your first data scientist will likely be a senior, broad hire rather than a junior specialist.
How people enter AI work is shifting, which matters for how you build the team after the first hire. Apprenticeships have risen from 3 percent of AI hires in 2020 to 19 percent in 2025, according to the AI Labour Market Survey 2025 report from DSIT. So the market leans senior for the first role, but there is a growing route to grow junior talent once that first person is in place.
Start with live postings, not a template. Pull together ten to fifteen current fintech data scientist adverts and read what they ask for. Group the recurring tasks: fraud models, pricing, risk scoring, product analytics, regulatory reporting. The patterns tell you what the market expects a first data scientist to own, and where you can be sharper than the crowd.
Then narrow ruthlessly. A first hire cannot do everything, so pick the one or two problems that move your business this year. Write the spec around those problems, not around a wish list of tools. Name the data you have, the decisions the role will improve, and who the person will work with. Clarity attracts the right people and filters out the wrong ones.
Finally, match the seniority to reality. Given the market leans to degree-level and above in financial services, expect a broad, senior profile who can set up foundations: clean data, a first model, and the judgement to know what to build next. Build the apprenticeship or junior layer later, once that person can mentor it.
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