Applied AI Scientist

As an Applied AI Scientist, you will bring scientific rigour to the design, evaluation, and improvement of cutting-edge AI solutions supporting Brit's operational simplification strategy. You will bridge the gap between classical data science, traditional machine learning, and modern Generative and Agentic AI methodsWorking alongside AI/ML engineers, data engineers, and business leaders, you will define quality standards, build evaluation frameworks, and translate complex AI outputs into practical, actionable business insights.

Responsibilities:

• AI Solution Development: Combine classical ML and cutting-edge Gen AI/agentic methods to solve key business challenges and drive operational efficiency.
• Proof-of-Concept and Experimentation: Plan and execute structured PoCs for generative and agentic AI systems covering large-scale prompt optimisation, fine-tuning, and model selection.
• Evaluation and Benchmarking: Design robust evaluation frameworks, define quantitative quality metrics, and construct benchmarks to test real-world AI performance.
• Quality and Safety Assurance: Establish groundedness, safety, and hallucination metrics to ensure trustworthy AI behaviour.
• Model Analysis: Analyse foundation model behaviour in dev/prod environments to identify failure modes, bias, and performance limits.
• Cross-Functional Collaboration: Translate complex AI concepts into clear, accessible insights for operational stakeholders and non-technical business partners.
• Agile Culture: Contribute to the AI Enablement operating model, adopting agile methodologies and building reusable AI best practices.

Requirements:

• Required: Bachelor's degree in a STEM field with experience applying data science in a commercial setting.
• Preferred: Master's degree or higher in a STEM field.

Technical Skills:

• Strong foundation in statistical modelling and classical machine learning.
• Hands-on experience with GenAI methods: prompt engineering, RAG, fine-tuning strategies, and LLM output assessment.
• Experience building and operationalising agentic AI solutions.
• Proven track record designing evaluation frameworks, benchmark construction, A/B testing, and quality metrics.
• Excellent data visualisation and technical communication skills.
• (Bonus) Familiarity with the insurance domain or experience in a regulated industry.

Skills

AI, GenAI, LLM, ML, RAG, fine tuning, finetuning, generative AI, large language model, machine learning, prompt engineering, retrieval augmented generation, scientist, statistical, statistics