Lead Product Data Scientist

About the Role

As our first Lead Product Data Scientist, you'll shape how Zego makes its biggest product and commercial decisions. You’ll work directly with senior leaders across Product, Growth and Operations to decide where to focus, which bets to back and how we’ll know they’ve worked.

You’ll start by building the foundations - a trusted experimentation framework and customer models that tell us who to acquire, how to engage them and where to invest. As teams learn to run routine experiments themselves, your focus will shift to guiding product investment and making sure evidence, rather than intuition, drives our roadmap.

This is a senior, hands-on role with real scope to shape a new capability - and the chance to use AI to scale data science across a growing business.

Responsibilities

• Work with senior leaders to embed data science in strategy and decision making. Create and own a prioritised roadmap for where data science can have the biggest impact.
• Own Zego’s experimentation framework - design standards, power, guardrail metrics and decision rules. Coach teams to run routine experiments themselves.
• Partner with Product teams before anything is built, shaping ideas into evidence-based hypotheses and well-designed experiments. Apply causal inference where randomised tests aren’t possible.
• Build models of customer behaviour across the lifecycle - acquisition, conversion, retention, churn and lifetime value - with teams such as Marketing and Fraud.
• Use AI to scale data science across Zego, automating experiment analysis and readouts. Build self-serve tools that help teams sharpen their hypotheses.
• Take the most impactful models into production with our ML Platform team.
• Set technical direction and mentor analysts and data scientists as the capability grows.

Requirements

• Significant data science experience in a product-led business, with a track record of work that changed product or commercial decisions.
• Strong executive presence - you influence senior leaders, challenge decisions with evidence and explain results clearly to any audience.
• Expertise in experimentation and causal inference, with many online experiments designed and analysed.
• Experience building customer models such as churn, propensity, LTV or segmentation - and getting them used in real decisions.
• Strong Python and SQL, with confidence working AI-first (and the judgement to spot when AI output is wrong).
• Experience taking models into production on AWS SageMaker or a similar ML platform.

Nice to Have

• Familiarity with experimentation or product analytics platforms such as Statsig or Amplitude.
• Experience with Snowflake, dbt and Looker or a similar modern data stack.
• Experience of marketing measurement such as media mix modelling or incrementality testing.
• A background in insurance, fintech or another regulated or subscription business.

Benefits

• Market-competitive salary, benchmarked against your function and reviewed every year.
• Annual performance bonus, linked to company performance and your contribution.
• Share options, a real stake in the company and a share in the future you build.
• Private medical insurance.
• Pension, generous holiday, and £1,000 a year to spend on getting to the office or learning something new.
• Cutting-edge systems and tools, so you always have what you need to drive your impact.