The wider hiring market is careful right now, and that shapes how insurers compete for specialist talent. Early estimates for May to July 2026 suggest vacancies fell by 6,000 (0.8%) to 707,000 across the UK, according to the ONS Vacancies and jobs bulletin for August 2026. The picture over the quarter is similar: the ONS reports a 7,000 (0.9%) drop for April to June 2026, with professional, scientific and technical activities among the sectors seeing the largest decreases.
That cooling headline hides a sharper story for technical roles. There are 2.5 unemployed people per vacancy, a ratio the ONS notes has held steady since mid-2025. So the market is not short of people in general. It is short of people who can build and challenge complex risk models, which is exactly the skill natural catastrophe modelling depends on.
Why does this matter for insurers? Because when the broad market softens, the scarce specialists get scarcer. Demand hotspots stay hot even as everything around them slows.
Nat cat modelling sits at the meeting point of data, science and pricing. The 2026 additions, climate-adjusted loss views, secondary perils like flood and wildfire, and independent validation of vendor models, all need people who can code, read the science and price the risk. That pulls insurers straight into the most competitive part of the labour market.
The scarce skills are the technical ones. Skills England's 2026 annual skills report finds digital and engineering occupations appear most commonly among priority occupations, many already facing high recruitment demand and expected to keep growing. Demand for AI skills, increasingly used to speed up model runs and stress-test assumptions, rose nearly 200% in a year, with London accounting for roughly two-thirds of all UK technology vacancies, according to Accenture data reported by The Register.
Data and specialist skills are the through-line. Computer Weekly's tech recruitment outlook for 2026 points to demand hotspots for specialist skills including AI and data even against a cautious backdrop. For a cat modelling team, that means exposure analysts, catastrophe risk modellers, pricing actuaries and data engineers are all competing in a national pool, not a sector one.
Start by describing the person, not just the tool. A strong cat modeller is curious about where a model is wrong, not only how to run it. Hire for that instinct and the vendor platform becomes a detail you can train.
Widen the pool on purpose. The people who can build these models are the same people every data-heavy sector wants, and Skills England flags these occupations as priority skills in short supply. Apprenticeships are one route worth taking seriously: the DSIT AI Labour Market Survey 2025 shows apprenticeships rose from 3% of AI hires in 2020 to 19% in 2025. Growing your own analysts is now a mainstream tactic, not a fallback.
Then move quickly and stay honest about the work. In a market with 2.5 unemployed people per vacancy overall but real scarcity at the top, per the ONS, a slow process loses the best modellers to a faster competitor. Be clear on the climate and secondary-peril work in the job itself: the people you want are drawn to the hard problems.
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