Lead Data Modeller

Job Title: AI Lead - Data & Agentic AI Foundations

Location: London

About the Job you are considering:
we are seeking AI Lead - Data & Agentic AI Foundations who will provide both strategic direction and technical leadership to establish the critical data, knowledge, and technology foundations required to enable AI-powered underwriting, claims, pricing, and operational decision-making.

This role will bridge traditional enterprise data platforms and emerging AI ecosystems, including Agentic AI, Knowledge Graphs, Intelligent Automation, and AI-driven decision support. Working closely with Data, Architecture, Technology, and Business stakeholders, the successful candidate will define the target-state AI architecture, establish enterprise AI-ready data foundations, and drive the adoption of scalable AI capabilities across the organization.

The role requires a rare combination of strategic vision, enterprise architecture expertise, hands-on technical leadership, and insurance domain knowledge.

Hybrid working:

The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of Company offices, client sites, and your home; noting that you will be unable to work at home 100% of the time.

Your Role:
AI Strategy & Transformation

• Define and execute the enterprise roadmap toward becoming an AI-native insurance organization.
• Develop long-term AI and data architecture strategies aligned with business objectives.
• Partner with the CDO, CIO, Data Engineering, Enterprise Architecture, and Underwriting leadership teams to prioritize AI investments and initiatives.
• Create phased roadmaps covering near-term, medium-term, and future-state AI capabilities.
• Identify opportunities where AI can improve underwriting effectiveness, claims efficiency, pricing sophistication, and operational productivity.
• Establish governance frameworks supporting responsible and scalable AI adoption.

Agentic AI Architecture & Foundations

• Design and implement foundational capabilities enabling enterprise Agentic AI adoption.
• Define standards for exposing business capabilities, enterprise applications, and data assets as reusable AI services and tools.
• Establish agent orchestration patterns, tool integration standards, and autonomous workflow architectures.
• Develop frameworks supporting AI agents, copilots, and intelligent assistants across business functions.
• Evaluate emerging AI technologies and agent frameworks to support enterprise-scale adoption.

Data as a Product Leadership

• Champion and institutionalize a Data as a Product operating model.
• Define AI-ready data products across:
  • Underwriting
  • Claims
  • Pricing
  • Actuarial
  • Risk Management
  • Operational Decision Support
• Ensure data products are discoverable, reusable, governed, trusted, and consumable by AI applications and agents.
• Drive transition from traditional reporting-focused data usage toward operational and decision-centric AI use cases.
• Promote product thinking across data teams and business stakeholders.

AI-Ready Data Platform Architecture

• Assess current Databricks and Enterprise Data Platform capabilities against AI requirements.
• Define enhancements required to support:
  • AI and GenAI workloads
  • Retrieval-Augmented Generation (RAG)
  • Large Language Models (LLMs)
  • Agentic workflows
  • Real-time intelligence solutions
• Design and implement operational data zones supporting:
  • Real-time and near real-time data access
  • AI inference workloads
  • Event-driven architectures
  • Intelligent automation solutions
• Establish approaches for exposing enterprise data through:
  • APIs
  • Data Services
  • Semantic Layers
  • AI Interfaces
  • Knowledge Platforms
    

Knowledge Management & Semantic Architecture

• Define enterprise strategies for managing structured and unstructured knowledge assets including:
  • Underwriting Guidelines
  • Risk Appetite Frameworks
  • Policy Documentation
  • Product Catalogues
  • Regulatory Documentation
  • Clause Libraries
  • Compliance Standards
• Lead the design and implementation of enterprise Knowledge Graphs.
• Establish semantic data models and contextual knowledge layers.
• Build scalable knowledge foundations supporting AI applications and intelligent reasoning.
• Ensure trusted, governed, and auditable access to contextual business knowledge.

Your Skills:
Essential

• 12+ years of experience in Enterprise Data, Data Architecture, AI, or Technology Leadership roles.
• Proven experience developing enterprise AI and data strategies.
• Extensive experience designing large-scale data platforms and modern data architectures.
• Strong experience with Databricks, cloud data ecosystems, and lakehouse architectures.
• Experience implementing AI, GenAI, Machine Learning, or Agentic AI solutions in enterprise environments.
• Strong understanding of Knowledge Graphs, Semantic Technologies, Taxonomies, and Metadata Management.
• Experience designing API-driven and service-oriented architectures.
• Expertise in Data as a Product principles and operational data architectures.
• Experience leading enterprise transformation initiatives.
• Strong stakeholder management and executive communication skills.
• Deep understanding of AI delivery, governance, and adoption challenges.

Desirable

• Insurance industry experience across Underwriting, Claims, Pricing, Risk, or Actuarial domains.
• Experience with Agentic AI platforms and orchestration frameworks.
• Experience implementing Enterprise Knowledge Graphs.
• Familiarity with Vector Databases, RAG architectures, and LLM ecosystems.
• Experience with Azure AI, OpenAI, AWS AI, or Google AI platforms.
• Knowledge of event-driven architectures and real-time data platforms.
• Experience delivering AI-powered decision support systems.