Tell us about your career before Zego. What specific expertise has been most useful here?
I've worked across a few different corners of insurance before landing in insurtech, touching underwriting, pricing, portfolio analysis, claims, and fraud along the way. The common thread through all of it was data. Every one of those disciplines lives and dies by the quality of the analysis behind it, whether that's pricing a risk accurately, understanding why a portfolio is deteriorating, or spotting patterns that suggest something fraudulent is happening. That breadth means I've always had to see the full picture rather than just one slice of it. What's translated most directly to Zego is knowing how to turn a messy portfolio into a clear story, and having the technical depth to own that process end to end.
What specific technical challenge made you choose Zego over other opportunities?
Honestly, it was the pace at which data actually gets used here. At a lot of traditional insurers, you can do brilliant analysis and watch it sit in a deck for six months. Zego felt different. The infrastructure was already there, the appetite for acting on insight was real, and the problems were genuinely hard. What really set it apart was the ability to leverage AI tools as a core part of how you work, not as a novelty bolted on after the fact, but genuinely embedded into how you solve problems and improve the quality of analysis. Combine that with a culture that actually moves on good ideas, and it became a pretty easy decision.
Describe a project where real-world insurance complexity met our tech capabilities. What was the hardest part to solve?
Quote manipulation is a good example: customers or intermediaries adjusting the information they provide to secure a better premium. The insurance complexity is real, the patterns are subtle, and you have to be careful not to penalise legitimate behaviour. But what made it interesting at Zego specifically was being able to bring the full data stack to bear on it. Querying at scale across pricing and policy data, layering in signals from enrichment data, and using AI tools to surface patterns that wouldn't be visible through traditional analysis. The hardest part wasn't finding signals, it was validating them rigorously enough to turn them into underwriting decisions rather than just interesting observations. That's where the tech capability actually matters: it shortens the distance between a hypothesis and something you can act on with confidence.
How does "High performance, done thoughtfully" show up in your peer reviews or code shipping?
I try not to treat speed and rigour as a trade-off. For me, high performance is end-to-end: it starts with noticing a problem in the first place, not waiting for it to be flagged, and it doesn't finish until there's a recommendation on the table that's actually actionable. That middle bit matters too, though. Understanding the wider landscape, what other priorities are in flight, what will genuinely move the needle versus what's interesting but low impact, is part of doing it thoughtfully. Showing up with a well-timed, well-evidenced recommendation that the business can act on is a very different thing from just producing good analysis. That's the standard I hold myself to.
Give an example of a time you were empowered to change a process or suggest a better way.
When discrepancies surfaced or data simply wasn't flowing through due to legacy blocks, rather than working around it I'd trace it upstream, identify where the restriction was introduced, and push for a fix at the source. Having a manager and team who actively encouraged that kind of interrogation, and gave me the space to follow the thread across teams, meant the fixes we landed were real fixes rather than workarounds. That trust made a real difference to the quality of what we could build on top of it.
What surprised you most about how Zegons collaborate after you joined?
How flat it actually is. I'd heard that before joining and every company says it, but the reality here matched the pitch. You can be in a direct conversation with pricing, product, and underwriting the same day something surfaces, without it needing to escalate through three layers first. Coming from more traditional environments, that speed of collaboration took some adjusting to. In a good way.
What advice would you give to someone moving from a traditional firm to Zego?
Bring your expertise, but hold your assumptions loosely. The domain knowledge matters because insurance is still insurance, but the pace and the way decisions get made here is genuinely different. You won't have six months to perfect something before it goes anywhere. The feedback loop is fast enough that you learn quickly, but you have to be comfortable moving before you feel completely ready. Once you experience the sheer pace of development here, and how quickly things actually ship and evolve compared to most insurers, it really is hard to imagine going back.