Applied Scientist

Lead Applied Scientist Generative AI & Agentic AI | Insurance | LondonLondon – hybrid, 3 days per week in the office£90,000–£120,000 base + 15–20% bonusPrevious insurance or reinsurance experience is required, ideally with an understanding of underwriting, claims, actuarial or other insurance data.This is a senior, hands-on position for an experienced Applied Scientist who wants to take scientific ownership of sophisticated AI solutions, with a particular focus on LLMs, Generative AI and agentic systems.You’ll act as a senior technical authority within the Data Science & AI team, setting the scientific approach and quality standards for AI solutions from initial problem framing and experimentation through to evaluation, validation and production monitoring.Importantly, this isn’t a role where you’ll move away from the technology. You’ll remain hands-on, tackling complex problems, developing models and AI solutions, writing production-quality Python and reviewing the work of other scientists and engineers.What you’ll be doing:

• Leading the scientific design and development of AI/ML, Generative AI and agentic AI solutions
• Designing LLM applications incorporating RAG, tool use, prompting and multi-step agent workflows
• Owning evaluation methodologies for LLMs, RAG and agentic systems, including metrics, test sets and acceptance thresholds
• Applying strong mathematical and statistical rigour to experimentation, uncertainty, model behaviour and validation
• Solving complex problems involving unstructured documents, expert workflow automation, forecasting, optimisation, portfolio analysis and claims
• Setting standards for experimentation, evaluation, coding and documentation
• Leading Responsible AI approaches covering bias, fairness, explainability and model risk
• Writing production-quality Python and contributing directly to higher-risk or novel AI projects
• Working with Databricks, MLflow and Spark alongside modern LLM and AI frameworks
• Mentoring Data Scientists and Engineers through technical reviews, pairing and scientific guidance
• Acting as the escalation point for challenging scientific and modelling decisionsWhat we’re looking for:You’ll need deep, hands-on experience with LLMs and Generative AI, including RAG, prompt engineering, fine-tuning and the design and evaluation of agentic AI systems.You’ll also need excellent Python skills and strong foundations across statistics, probability, machine learning, experimental design and model validation.Crucially, you should have experience developing robust evaluation frameworks for AI systems – particularly measuring the quality and reliability of LLM, RAG and agent outputs – rather than simply building prototypes.Experience taking AI solutions from experimentation through to production is essential, alongside practical exposure to technologies such as Databricks, MLflow and Spark.Previous insurance or reinsurance experience is required, ideally with an understanding of underwriting, claims, actuarial or other insurance data.A postgraduate qualification in Computer Science, Statistics, Mathematics or another quantitative discipline would be advantageous.