Lead Underwriting Data Scientist | Portfolio Analytics | InsurTech | Up to £105k+ bonus

? Official Role Title: Lead Portfolio Analyst

? Location: London or Hampshire (2 days a week in-office)

? Salary: £80k - £105k + 10% bonus

Who are they?

A well-backed InsurTech leading the UK short-term motor insurance market, one hour to one month cover, at serious volume. They feel like a start-up in the best sense, small team, real ownership, people who care about doing good work. Collaborative but autonomous, they hire people who take ownership and get things done.

What's the role?

You'll sit in the underwriting team, working closely with the underwriting manager and Chief Underwriting Officer, building and refining the analytical logic that helps their panel of underwriters make better decisions on coverage, rates, and portfolio footprint. Around 70% heads-down individual contribution, 30% leading projects and developing junior analysts.

On any given week you might be:

• Analysing portfolio performance and identifying where coverage, rates or footprint need adjusting
• Building fraud detection models and identifying new risk signals at point of quote
• Working with the panel of underwriters to improve how they assess and select risk
• Exploring non-traditional data sources to sharpen underwriting decisions
• Getting models into production so they're shaping live underwriting decisions every day
• Helping junior analysts develop their thinking and technical skills

It's a role where your work ends up in the product, driving real underwriting decisions on live policies every day.

Who are they looking for?

This is a rare role and they know it. They're looking for someone who brings genuine breadth across underwriting analytics, pricing, and data science, someone who understands how insurance portfolios actually behave and can work credibly with underwriters, not just alongside them.

Your background might span underwriting analytics, actuarial work, pricing, or a mix of all three. What matters is that you think rigorously about risk, can build and deploy models that shape real decisions, and can explain complex findings clearly to senior stakeholders.

On the technical side, Python or R, SQL, and experience with modelling techniques like GBMs are important. Fraud analytics experience is a plus, as is data enrichment, though fresh thinking on both counts matters just as much as direct experience. Cloud data warehouse experience such as BigQuery or Azure is useful but not essential.

And the good stuff!

? 10% discretionary bonus + annual pay reviews

? Up to 22 \"work from anywhere\" days per year

? Private medical cover + critical illness insurance

? Employer pension matching up to 7.5%

? 25 days holiday, rising to 30 with tenure, plus 2 \"my time\" days

? £300 one-off WFH setup budget

? EV scheme + cycle to work

? Market-leading parental leave across all types

? Dedicated learning and training budget

If this sounds like your kind of place, we'd love to hear from you.

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