Applied AI Scientist

About the Role

As an Applied AI Scientist at BRIT, you will be responsible for applying classical data science and machine learning techniques, as well as generative and agentic AI methods, to solve business problems and support the company's operational simplification strategy.

You will work with a team of highly skilled professionals to design and implement structured proof-of-concept solutions for generative AI and agentic systems, and translate complex AI outputs into clear, accessible insights for operational and business stakeholders.

Additionally, you will analyze foundation model behavior in development and production settings, identify failure modes, bias patterns, and performance boundaries, and improve confidence in AI solutions by establishing robust evaluation methods and clear quality thresholds for operational use cases.

Key Responsibilities

• Apply classical data science and machine learning techniques to solve business problems and support operational simplification strategy.
• Plan and implement structured proof-of-concept solutions for generative AI and agentic systems.
• Translate complex AI outputs into clear, accessible insights for operational and business stakeholders.
• Design and implement evaluation frameworks for AI systems, constructing benchmarks and defining quality metrics.
• Analyze foundation model behavior in development and production settings, identifying failure modes, bias patterns, and performance boundaries.
• Improve confidence in AI solutions by establishing robust evaluation methods and clear quality thresholds for operational use cases.
• Contribute to the development of applied AI best practices by documenting findings, evaluation approaches, and reusable methods for future use cases.

Skills & Qualifications

• Bachelor's Degree or higher in a STEM field.
• Strong theoretical understanding and experience with statistical modelling and classical data science including traditional machine learning models.
• Strong technical understanding and experience with Gen AI methods; prompt engineering, RAG, fine-tuning strategy, LLM output assessment.
• Experience with building and operationalizing agentic AI solutions.
• Experience with evaluation framework design; benchmark construction, A/B testing, quantitative quality metrics for AI outputs.
• Strong data visualization and insights communication skills.
• Strong ability to translate between scientific and business language for non-technical stakeholders.
• Insurance domain familiarity and experience working in a regulated industry is a plus.

What You'll Learn

In this role, you will have the opportunity to learn and apply cutting-edge AI techniques, such as generative and agentic AI methods, to real-world business problems.

You will also develop your skills in data visualization, insights communication, and evaluation framework design, and gain experience working in a regulated industry.

Additionally, you will contribute to the development of applied AI best practices and document your findings, evaluation approaches, and reusable methods for future use cases.

Resume Tip

When applying for this role, be sure to highlight your experience with statistical modelling, classical data science, and Gen AI methods, as well as your ability to translate between scientific and business language.

Also, be sure to emphasize your experience working in a regulated industry and your ability to work well within a team environment.