Data Scientist

Build AI solutions that sit across pricing, underwriting, and claims data for a global insurer operating across 54 countries.

You'll work inside a Digital team delivering predictive analytics and AI solutions that help underwriters, actuaries, and business leaders make faster, better-informed decisions. The work spans the full lifecycle, from scoping problems and integrating data sources through to model development, deployment, and adoption across a global platform.

The team works in an agile environment with a modern stack, Databricks, Snowflake, Python, and the usual ML libraries like XGBoost, LightGBM, and scikit-learn. Flexibility is genuinely on the table here, and the organisation has a strong track record of accommodating different working arrangements. You'll be working with a global analytics function, which means regular collaboration across time zones and exposure to how data science gets done at real enterprise scale.

What makes this role different from a standard senior data scientist position is the LLM and AI component. They're building tools that use large language models to enrich data, automate workflows, and surface insights for end users, not just chatbots. If you've worked on transformation projects where AI changed how a business actually operates, that experience will land well here. General insurance background is a genuine differentiator, and if you've navigated a large, multi-stakeholder global organisation before, you'll settle in faster.

What You'll Do

• Build and deploy predictive models across pricing, underwriting, and claims using Python, GLMs, gradient boosting, and deep learning techniques.
• Develop AI solutions that integrate internal and external data sources to surface decision-ready insights for business and actuarial teams.
• Manage end-to-end ML pipelines including deployment, monitoring, drift detection, and retraining within Databricks and Snowflake environments.
• Present model performance and portfolio insights to senior business, actuarial, and global analytics stakeholders across multiple time zones.

What You'll Need

• 5+ years of hands-on data science experience with strong Python skills across pandas, scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
• Practical experience with LLMs applied to data enrichment, workflow automation, or process improvement in an enterprise setting.
• Experience on Databricks and Snowflake, with solid MLOps practices including deployment, monitoring, and retraining.
• Background in general insurance or a large global organisation is highly regarded; exposure to agentic AI is a bonus.

About the Company

They're one of the world's largest insurance groups, operating across 54 countries with around 40,000 employees globally. The Digital team sits at the centre of a serious push to apply AI and advanced analytics across underwriting, pricing, and claims at enterprise scale.

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