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Head of Telematics Data ScienceZego · London
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Zego

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Head of Telematics Data Science

🗓️ Posted 2026-09-16 London Fulltime Hybrid ICT
💰 Salary Not shared 📊 Market 8,333 GBP–12,500 GBP/mo

About the Company

At Zego, we're on a mission to do the good thing, not the insurance thing. Insurance hasn't changed much in over a century. The way we live, work and travel has. We're building the real-time, AI-driven infrastructure that powers innovative insurance, so good drivers get cover that works the way they actually drive. We're not just updating insurance; We're leading the AI evolution in insurance. For us, AI isn't a line on a roadmap or a buzzword on a slide. It's our operating reality, and it's how we build products that back drivers instead of the old insurance playbook. We don't do things slowly, and we don't do bureaucracy. We back high-performance builders who want ownership, early responsibility and the chance to do the most career-defining work of their lives.

About the Job

The Telematics team uses digital signal processing and machine learning to turn raw signal into a precise picture of how people actually drive. We use it to price insurance more fairly, to help drivers be safer on the road, and to power Zego Rewards, everyday rewards for good driving that turn car insurance into an engaging product rather than a box to tick. As Head of Telematics Data Science you will set the research agenda and own the technical direction of that work, reporting to the Director of Data.

Role & Responsibilities

  • ● Define and drive the technical roadmap, making sure the team is working on the highest-impact problems
  • ● Lead the development of novel models to detect human behaviour and vehicle kinematics from high-frequency phone sensor data, on-device and in the cloud
  • ● Act as the primary technical authority on telematics across the business, advising on existing products, surfacing patterns nobody has seen before
  • ● Continuously push the predictive power of our existing algorithms and ML models
  • ● Tell drivers apart from passengers using sensor data alone
  • ● Detect crashes and estimate their severity
  • ● Bring map and road-context data into our models in a way that genuinely improves prediction
  • ● Handle handset and sensor heterogeneity, so a model behaves consistently on a five-year-old Android and a new iPhone
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  • ● Do on-device ML under real battery and compute constraints - trip detection already runs on the phone, and more could
  • ● Make behavioural risk models transfer to new markets, starting with Japan