Lead AI Product Manager - Product Vertical

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 product strategy across multiple teams and domains, including the build, buy or partner calls that shape Zego's competitive position.
• Define and validate new revenue mechanics, from 0 to 1 propositions to upsell and retention loops, grounded in unit economics and customer insight rather than gut feel.
• Embed rigorous experimentation as the default operating rhythm, so rate of learning replaces opinion as the leading metric.
• Build reusable AI playbooks, workflows and assets that multiply the whole team's output, not just your own.
• Design the product metrics and dashboards the business runs on, and use financial, behavioural and usage data to ground every strategic decision.
• Own company-level programmes with direct P&L impact, accountable for commercial outcomes rather than feature delivery.
• 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.
• Experiment-first operator. You have built experiment cultures, not just run individual tests, and you make build, measure, learn the default rhythm for your teams.
• Commercial and financial fluency. Deep understanding of unit economics, pricing levers and revenue modelling, and credible with Finance and Leadership on P&L-level decisions.
• Data mastery. You design the metrics and dashboards the business relies on, and you are as comfortable in Snowflake as you are in a strategy review.
• Technical depth. Comfortable engaging engineers on system architecture, APIs and ML pipelines, enough to make informed strategic bets.
• 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.