Lead AI Product Manager - Telematics

Job description

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.

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.

Responsibilities

• Set the six-to-12-month Telematics strategy across the SDKs, data processing and Risk Management, including the build, buy or partner calls on sensors, data and models.
• Own data quality at the source, setting the standards for trip recording, location accuracy, battery impact and stability that every score depends on.
• Own the product side of our scoring models with Data Science, so the score is accurate, explainable and safe to change on a live book.
• Lead Risk Management, defining how we act on driving behaviour - from price adjustments to interventions and cancellations - balancing loss ratio with fair customer outcomes.
• Turn driving data into P&L impact with Pricing, Underwriting, Claims and Fraud, through sharper risk selection, fairer prices and better fraud detection.
• Embed rigorous experimentation as the default operating rhythm, even when changes touch price and live policies.
• Build reusable AI playbooks, workflows and assets that multiply the whole team’s output, from trip-data analysis to automated data and model monitoring.
• Coach Product Managers across the organisation, raising the bar for product craft and data capability.

Requirements

• Strategic product leadership. Significant experience leading product strategy across multiple teams or domains in a high-growth environment, with decisions that moved business-level metrics.
• Data, sensor or ML products. You’ve built products on behavioural, location or sensor data - ideally telematics, mobility or IoT - and understand how signals become models, and models become decisions.
• Technical depth. Comfortable engaging engineers on mobile SDKs, data pipelines and ML pipelines, enough to make informed strategic bets.
• Commercial and risk fluency. Deep understanding of pricing levers, risk modelling and loss ratios, and credible with Actuarial, Finance and Leadership.
• Data mastery. You are as comfortable in Snowflake as you are in a strategy review, and you can interrogate model performance, not just read a dashboard.
• Experiment-first operator. You have built experiment cultures, not just run individual tests.
• You work AI-first. You will use AI daily here, and we mean daily. You do not need to arrive an expert, but you do need to arrive curious, experiment fast, and take ownership of getting good quickly. People who wait to be trained will find this uncomfortable.