Staff Data Scientist

Overview

In this role, you provide technical leadership for data science within Claims, shaping risk decisioning and fraud detection. You will design and deploy production ML and Generative AI/LLM systems to support claims validation and automation. You’ll work with Claims data scientists and cross-functional partners to scale decisioning, ensure robust monitoring and governance, and drive measurable automation impact. This is an opportunity to influence platform choices and deliver high-quality, end-to-end AI-enabled solutions that improve the customer journey.

Pay / Benefits

• Flexible working in London office 3 days a week
• Competitive bonus scheme
• Flexible benefits budget (Ben Mastercard)
• Mental wellbeing support via Oliva
• Learning and development budgets plus 2 extra days per year
• Private health care and Vitality benefits

Responsibilities

• Provide technical leadership across Claims Fraud and risk decisioning with Product and Engineering
• Design, build and iterate production ML and Generative AI/LLM systems for claims validation and automation
• Collaborate with Claims data scientists to align models, data, and workflows for scalable decisioning
• Advocate for platform and tooling investments (monitoring, QA, feedback loops) to enable AI-driven end-to-end automation
• Influence technical direction to align with multi-year automation vision for claims handling
• Set high bar for statistical rigour, experimentation, and measurement for senior stakeholders

Key requirements

• Significant commercial experience delivering end-to-end ML solutions from problem framing to deployment and monitoring
• Hands-on experience building and shipping Generative AI systems in production with evaluation and integration into workflows
• Strong statistical foundation with experience in risk-based decisioning under uncertainty (fraud/credit/regulated domains)
• Proven ability to influence technical direction across Data Science and Engineering
• Strong stakeholder management and ability to push back constructively with Product and Engineering

• strong communication and influencing skills
• ability to challenge assumptions and think systemically
• resilience in ambiguity and change
• End-to-end ML in production
• Generative AI systems/LLMs in production
• risk-based decisioning and uncertainty handling

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