Associate Director, Data Science

• Build, lead, coach and retain a diverse team of data scientists and AI engineers, creating clear standards, career pathways and a culture of learning, experimentation and accountable delivery.

• Own the unit strategy, capability roadmap and delivery portfolio for advanced analytics, machine learning, Generative AI and Agentic AI, aligned to business priorities and enterprise architecture.

• Translate business needs into responsible AI products with measurable outcomes, from discovery and prototyping through production deployment, adoption, monitoring and continuous improvement.

• Create durable partnerships with business users, Technology, Architecture, Cybersecurity, Data, Risk, Compliance, Group Office, external partners and vendors.

• Ensure every production solution has the governance, documentation, operating model and support structure required for reliable and sustainable use.

• Initiate collection of new data and the refinement of existing data sourceswith the Data Modelling teamif new data is requiredforAImodel development

• Possess strong stakeholder management and communication skills with experience working with senior leadership team / EXCO members.

• Collaborate with potential partner(s) / vendor(s) or internal teams to develop an efficient prototype, conduct assessment of the AI solution, give feedback, determine areas of improvement and finalize the design, to automate or streamline operations within the Company.

• Bachelor's orMaster's degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering or a related discipline, or equivalent practical experience.

• Typically,10+ years of relevant experience across data science, machine learning, AI engineering or advanced analytics, including meaningful experience leading and developing technical teams.

• Demonstrated delivery of AI or analytics products from problem framing and prototyping through production, adoption and ongoing support.

• Strong applied knowledge of Python and SQL. Ability to review solution designs, code, experiments and evaluation results, and to guide technical decisions without needing to be the primary developer for every solution.

• Current understanding of modern GenAI architecture, including LLMs or SLMs, RAG, vector search, chatbot or copilot design, agentic workflows, evaluation, guardrails andLLMOps.

• Experience partnering with enterprise Technology, security, architecture, data and risk functions in a regulated or complex environment.

• Executive-level communication, business consulting,prioritisationand stakeholder management skills, with the ability to explain complex AI topics clearly to non-technical audiences.

• Commercial and delivery acumen, including vendor management, budgeting, benefits tracking and portfolio trade-offs.

• Experience in life insurance, financial services, healthcare or another highly regulated industry.

• Knowledge of insurance use cases such as underwriting, claims, fraud or anomaly detection, customer and agent analytics, next-best action, service automation, retention and operational productivity.

• Experience with cloud AI and data platforms,lakehousearchitecture, distributed processing, APIs, containers and enterpriseDevSecOpspractices.

• Exposure to knowledge graphs,GraphRAG, multimodal models, synthetic data, privacy-enhancing technologies, causal inference,optimisationor digital experimentation.

• Relevant professional certifications in cloud, data, AI, architecture, security, agile delivery or service management.

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