Applied Scientist

i3; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

i3, a London-based insurer, seeks a Lead Applied Scientist to own AI/ML, Generative AI and agentic AI initiatives. The role is hands-on, developing production-grade Python solutions while guiding scientific strategy and quality for AI systems in insurance data contexts.

Based in London with a hybrid model (3 days in the office), you will design, evaluate and deploy advanced AI solutions, mentor junior staff, and lead responsible AI practices across bias, fairness and risk.

Qualifications

• Hands-on experience with LLMs and Generative AI, including RAG, prompt engineering and fine-tuning.
• Excellent Python skills and foundations in statistics, probability, ML, experimental design and model validation.
• Experience building evaluation frameworks for AI systems, measuring quality and reliability of LLM/RAG/agent outputs.
• Production experience: taking AI solutions from experimentation to production; Databricks, MLflow, Spark experience.
• Insurance or reinsurance experience preferred; understanding underwriting, claims data or related insurance data.
• Postgraduate qualification in CS, Statistics, Mathematics or quantitative discipline advantageous.

Responsibilities

• Lead the scientific design and development of AI/ML, Generative AI and agentic AI solutions.
• Design LLM applications with RAG, tool use, prompting and multi-step agent workflows.
• Own evaluation methodologies for LLMs, RAG and agentic systems, including metrics and acceptance thresholds.
• Apply rigorous statistics to experimentation, uncertainty, model behavior and validation.
• Tackle complex problems with unstructured documents, automation, forecasting and portfolio analysis.
• Set standards for experimentation, evaluation, coding and documentation.
• Lead Responsible AI practices covering bias, fairness, explainability and model risk.
• Write production‑quality Python and review work of other scientists and engineers.
• Work with Databricks, MLflow and Spark alongside modern LLM/AI frameworks.
• Mentor Data Scientists and Engineers through technical reviews and guidance.

Skills

LLMs & Generative AI

Python proficiency

Statistics & experimental design

Education

Postgraduate qualification in Computer Science/Statistics/Mathematics

Tools

Databricks

MLflow

Spark

Job description

Lead Applied Scientist – Generative AI & Agentic AI | Insurance | London

London – hybrid, 3 days per week in the office

Previous insurance or reinsurance experience is required, ideally with an understanding of underwriting, claims, actuarial or other insurance data.

I’m recruiting for a Lead Applied Scientist to join a leading organisation within the insurance/reinsurance market in London.

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 decisions

What 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.

London – minimum 3 days per week in the office