Lead Product Data Scientist
Role Overview
The central Data team works on projects across the business, including AI tools, dashboards, and customer insights.
This is the central Data team's first data science role, focused on opportunities such as experimentation, churn, marketing, and fraud.
The Lead Product Data Scientist will shape product and commercial decisions and establish a new data science capability.
Key Skills for This Role
Full Job Posting
About Zego
Zego describes itself as an AI-driven insurance business building real-time infrastructure and products for drivers.
The company says it uses AI in its operations and supports high-performance teams with ownership and responsibility.
Team and role
The central Data team works on projects across the business, including AI tools, dashboards, and customer insights.
This is the central Data team's first data science role, focused on opportunities such as experimentation, churn, marketing, and fraud.
The Lead Product Data Scientist will shape product and commercial decisions and establish a new data science capability.
What you will do
- Embed data science in strategy and decision making, and own a prioritized roadmap for its highest-impact opportunities.
- Own the experimentation framework, including design standards, statistical power, guardrail metrics, and decision rules; coach teams to run routine experiments.
- Partner with Product to frame evidence-based hypotheses and experiments, using causal inference when randomized testing is not possible.
- Build customer lifecycle models for acquisition, conversion, retention, churn, and lifetime value with partner teams.
- Use AI to automate experiment analysis and create self-serve tools for improving hypotheses.
- Take impactful models into production with the ML Platform team, set technical direction, and mentor data colleagues.
What you will need
- Significant data science experience in a product-led business and a track record of influencing product or commercial decisions.
- Strong influence and communication skills with senior leaders and varied audiences.
- Expertise in experimentation and causal inference, including designing and analysing many online experiments.
- Experience building and operationalizing customer models such as churn, propensity, lifetime value, or segmentation.
- Strong Python and SQL, sound judgment about AI-generated output, and experience deploying models with AWS SageMaker or a similar ML platform.
Nice to have
- Familiarity with Statsig, Amplitude, or similar experimentation or product analytics platforms.
- Experience with Snowflake, dbt, Looker, or a similar modern data stack.
- Marketing measurement experience, such as media mix modeling or incrementality testing.
- Background in insurance, fintech, or another regulated or subscription business.
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