Data Scientist

Overview

As a Data Scientist at AXA Health, you design, develop and deploy AI solutions that create commercial value and improve customer wellbeing. You will lead end-to-end AI projects across the customer journey, from strategy to deployment, while ensuring responsible AI and regulatory compliance. You’ll collaborate with cross-functional teams to translate insights for non-technical stakeholders and continuously optimise models. This role offers impact at scale within a mission-driven health insurance environment.

Pay / Benefits

• flexible working arrangements
• hybrid work options
• opportunity to work in a leading health insurer
• equal opportunities employer

Responsibilities

• Lead end-to-end data science and AI projects from problem framing to deployment
• Apply AI/ML methods across multimodal data along the customer journey (acquisition, retention, servicing, claims)
• Communicate findings clearly to non-technical audiences and inspire stakeholders
• Own and monitor ML/AI models, tracking performance, data drift, and latency
• Ensure regulatory compliance and champion AXA’s Responsible AI principles
• Pursue ongoing professional development and share knowledge with the team

Key requirements

• Master’s degree or equivalent in a numerical field
• Experience with supervised, unsupervised and deep learning for business problems, preferably in financial services or regulated industries
• Hands-on experience delivering end-to-end AI projects from model to deployment and business impact
• Proficiency in Python and SQL; strong data storytelling skills
• Experience with generative AI, including LLMs, prompt engineering, fine-tuning, RAG, and AI agents
• Cloud experience (Azure, Databricks) and familiarity with Azure DevOps (Boards, Repos, Pipelines)
• Knowledge of explainable AI, MLOps and LLMOps best practices
• Experience working in agile, cross-functional teams with ethical AI practices
• Eligibility to work in the United Kingdom

• Clear communication to non-technical audiences
• Collaboration within cross-functional teams
• Ethical and responsible AI mindset
• Supervised, unsupervised and deep learning
• Generative AI and LLMs, prompt engineering, fine-tuning, RAG, AI agents
• Azure and Databricks

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