Lead Data Scientist

Overview

As Lead Data Scientist, you will shape and deliver advanced data science solutions across the business, leading a team to turn data and models into scalable outcomes. You’ll partner with stakeholders to identify high-impact problems in areas like telematics and claims, and own projects from concept to production monitoring. You’ll drive robust modelling, data sourcing, and governance while fostering innovation across the Data Science function. This role offers scope to influence pricing strategy and business decisions at scale, within a collaborative, data-driven culture.

Pay / Benefits

• salary based on experience with car allowance
• hybrid working
• annual performance bonus
• private medical insurance
• pension contribution match up to 10%
• 28 days annual leave plus bank holidays

Responsibilities

• Lead and develop a high-performing data science team
• Shape and execute the Data Science strategy aligned to business priorities
• Develop advanced ML and statistical models to solve business problems
• Own projects end-to-end from concept to deployment and monitoring
• Collaborate with senior stakeholders to identify high-impact opportunities
• Explore new data sources, techniques, and technologies
• Ensure models are robust, scalable, and well-governed with strong validation and monitoring
• Partner with Data Engineering/Technology to build data pipelines and production deployment
• Translate complex outputs into clear insights for technical and non-technical audiences
• Champion best practices and drive continuous improvement across the function

Key requirements

• Proven leadership and mentoring of Data Scientists
• Strong ML, statistical modelling, and predictive analytics expertise
• Strong Python and SQL skills with commercial solutions experience
• End-to-end lifecycle experience for ML models (development to production)
• Experience with large, complex datasets
• Experience with cloud/DS tech (AWS, Azure, Databricks, Snowflake, Spark or similar)
• Strong stakeholder management and ability to influence decisions
• Good commercial awareness linking DS work to business outcomes

• stakeholder management
• clear communication of complex concepts
• influence and collaboration
• machine learning
• statistical modelling
• predictive analytics

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