This is a key leadership role within a global insurance and reinsurance business, sitting as part of a wider technology leadership team. The position is focused on building and leading a growing Data Science & Machine Learning function, while delivering impactful AI-driven solutions across the organisation.
You’ll play a central role in shaping and scaling AI capabilities, working on a range of use cases spanning advanced analytics, machine learning, and emerging areas such as Generative AI and AI agents.
Alongside leadership responsibilities, this role remains hands‑on, with involvement in architecting, designing, and delivering production-grade data science solutions using a mix of proprietary models, cloud platforms, and LLM-based approaches.
Key Responsibilities:
Leadership & Team Development
- Build, lead, and mentor a growing team of Data Scientists and ML Engineers
- Take ownership of performance, development, and engagement across the team
- Foster a strong, collaborative, and high‑performing team culture
- Support hiring and help shape the future structure of the function
Technical Delivery & Architecture
- Remain hands‑on in designing and building ML/AI solutions end‑to‑end
- Work closely with engineering teams to deploy scalable, production‑ready models
- Contribute to the development of robust MLOps practices, including monitoring, evaluation, and model lifecycle management
- Partner with data teams to define and build the data pipelines required for advanced analytics
Strategy, Innovation & Stakeholder Engagement
- Work with senior stakeholders to identify high‑impact AI use cases
- Translate complex business problems into data science solutions
- Drive adoption of modern AI techniques, including LLMs and advanced ML methods
- Communicate insights and solutions clearly to both technical and non‑technical audiences
Governance & Best Practice
- Ensure all AI and data solutions align with internal governance and regulatory standards
- Promote best practices in data science, experimentation, and model development
- Maintain a strong focus on data privacy, model transparency, and ethical AI use
Desired experience includes:
- Strong hands‑on experience building and deploying machine learning models in production
- Solid understanding of cloud‑based AI/ML ecosystems and modern data platforms
- Experience working with LLMs, Generative AI, or NLP/computer vision use cases
- Knowledge of MLOps practices, including CI/CD, monitoring, and model lifecycle management
- Excellent stakeholder management and communication skills
- Passion for mentoring and developing others
Experience in regulated environments (e.g. financial services or insurance) is beneficial but not essential.
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