Applied Scientist

• Lead Applied Scientist – Generative AI & Agentic Systems
• City, London/ Hybrid – 3 days per week in the office
• We are looking for an exceptional

Lead Applied Scientist to join the Data Science & AI function of a major international organisation investing heavily in

Generative AI, Large Language Models and Agentic AI .

This is a senior, hands-on position for an accomplished Applied Scientist who combines deep expertise in modern AI with strong mathematical and statistical foundations.

You’ll act as a scientific authority for AI , setting the standards for how solutions are designed, evaluated, validated and monitored.

You’ll work on some of the organisation’s most challenging AI problems while providing technical leadership and mentorship to Data Scientists and AI Engineers.

The focus is increasingly on

LLMs and agentic systems , including RAG, tool use and multi-step agent workflows. However, this role is fundamentally about selecting the right scientific approach for each problem , drawing on machine learning, deep learning, statistics and optimisation where appropriate.

This is not a purely advisory or leadership position. You’ll remain highly hands-on , building models and AI applications, writing Python, designing experiments, reviewing code and solving technically challenging problems.

Your responsibilities will include

• Leading the scientific design and development of

AI and machine learning solutions , translating complex business problems into measurable technical objectives.

• Designing sophisticated

Generative AI and Agentic AI systems , including prompting, RAG, tool use and multi-step agent workflows.

• Defining how LLM and agentic solutions are evaluated, including metrics, test sets, benchmarks, acceptance thresholds and evaluation frameworks .
• Applying mathematical and statistical rigour to experimentation, uncertainty, error analysis and solution validation.

Acting as the technical escalation point for particularly complex or ambiguous AI problems.

• Holding scientific ownership for solution quality and ensuring approaches are robust before deployment.
• Solving challenging problems involving unstructured documents, expert workflow automation, forecasting, optimisation, portfolio analytics and claims analytics .
• Selecting the appropriate approach for each use case, whether that involves LLMs, agentic AI, deep learning, traditional machine learning or statistical methods.
• Building production-quality solutions directly, particularly for novel, technically challenging or higher-risk projects.
• Developing approaches to improve the accuracy, reliability and robustness of LLM and agent outputs .
• Defining monitoring approaches to identify model degradation and drift once solutions reach production.
• Leading

Responsible AI practices, including bias and fairness testing, explainability and validation of model and agent behaviour.

• Setting standards for experimentation, evaluation, coding and technical documentation.
• Reviewing code, experiments and technical outputs produced across the wider team.
• Mentoring and developing Data Scientists and AI Engineers through pairing, technical reviews and knowledge sharing.
• Working closely with engineering leadership on architecture, deployment and productionisation.
• Communicating technical methods, results, limitations and trade-offs clearly to senior business stakeholders and governance teams.

You should bring

• Deep practical expertise with

Large Language Models and Generative AI .

• Strong experience with prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning and LLM evaluation .
• Hands-on experience designing and evaluating agentic AI systems , including agents that use tools and operate across multi-step workflows.
• Experience with modern AI/ML and data science libraries and frameworks, alongside technologies such as pandas, Num Py and scikit-learn .
• Strong mathematical and statistical foundations, including probability, statistics, linear algebra and optimisation .
• The ability to reason rigorously about uncertainty, error, model behaviour and statistical significance.
• Strong machine learning fundamentals, including model validation and experimental design.
• Experience creating evaluation and validation frameworks for AI systems , particularly LLM, RAG and agent outputs.
• A proven track record of taking AI solutions from prototype through to production .
• Practical experience with

Databricks, MLflow and Spark-based data processing .

• Knowledge of Responsible AI practices, including bias, fairness, explainability and model risk.
• Experience setting technical or scientific standards and developing other scientists and engineers.
• The communication skills to explain highly complex AI concepts to both technical and non-technical senior stakeholders.
• Agent frameworks and orchestration technologies for building multi-step, tool-using AI systems .
• Advanced approaches to LLM and agent evaluation.
• AI observability and production monitoring.
• Experience within insurance or reinsurance , particularly involving underwriting, claims or actuarial data.
• A postgraduate qualification in

Computer Science, Statistics, Mathematics, Data Science or another quantitative/computational discipline .

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