Data Strategy Lead, Digital Client

• Define the DCX data and knowledge strategy and roadmap for converting proprietary information into reusable knowledge and data products
• Prioritize high-value domains and use cases with business, content, product, and operations partners
• Build reusable knowledge products across client intelligence, claims intelligence, exposure data, risk engineering insight, benchmarking, policy wording, market appetite, and industry risk profiles
• Embed analytics and insight into workflows, processes, and day-to-day decision-making
• Define the knowledge layer, including business entities, relationships, taxonomies, ontologies, semantic standards, and entity resolution
• Establish governance, trust, evaluation standards, and operating models for AI-ready knowledge products
• Support AI and automation for discovery, classification, metadata extraction, data profiling, duplicate detection, and corpus preparation
• Create feedback loops to improve metadata, retrieval, quality, and usability of knowledge assets
• Define AI-readiness requirements for reports, presentations, contracts, broker notes, risk assessments, policy wording, claims summaries, and client deliverables
• Partner with engineering teams to shape tools that search, retrieve, query, summarize, compare, and evaluate knowledge assets
• Contribute to priority DCX work streams and translate ideas into practical solutions

• Proven experience in data strategy, data product management, enterprise data platforms, knowledge management, AI enablement, or digital product leadership
• Ability to bridge analytics and operations, translating data insight into operational practice and vice versa
• Strong understanding of retrieval-augmented generation, vector search, metadata, semantic layers, knowledge graphs, and agentic workflows
• Experience with complex enterprise data environments, fragmented systems, inconsistent data quality, and mixed structured and unstructured sources
• Ability to partner with senior business leaders, technology teams, data governance, legal, compliance, and product teams
• Experience defining data ownership, stewardship models, business glossaries, data quality frameworks, or domain data products
• Strong product mindset connecting technical enablement to business value and user adoption
• Ability to operate in ambiguity and create structure across complex, cross-functional environments
• Experience in financial services, insurance, risk advisory, professional services, or another data-rich regulated industry
• Familiarity with modern lakehouse, data catalogue, data governance, and AI platform architectures
• Familiarity with Databricks or comparable unified data and AI platforms, including Spark-based lakehouse environments
• Experience preparing proprietary document corpora for AI search, summarization, and reasoning
• Exposure to ontology design, knowledge graphs, semantic modeling, or entity resolution
• Working knowledge of enterprise AI governance, model evaluation, responsible AI, or AI risk controls
• Hands‑on experience building or scaling data products for client‑facing or colleague‑facing digital platforms
• Experience enabling teams to adopt new data, knowledge, or AI capabilities in sustainable ways

Demonstrates expertise in data strategy, knowledge management, and AI enablement, with a strong focus on building reusable knowledge products and embedding analytics into operational workflows. Proven ability to partner with cross-functional teams to drive data quality and governance in complex enterprise environments.

• Data Strategy
• Knowledge Management
• AI Enablement
• Data Governance
• Product Management

• Data Product Management
• Retrieval-Augmented Generation
• Metadata Management
• Knowledge Graphs
• Entity Resolution
• Data Quality Frameworks
• Ontology Design
• AI Risk Controls
• Data Profiling
• Document Corpora Preparation

• Operational Practice Translation
• Cross-Functional Collaboration
• Adaptability in Ambiguity
• User Adoption Focus
• Stakeholder Partnership

• Financial Services
• Insurance
• Risk Advisory
• Professional Services
• Data-Rich Regulated Industry

• Databricks
• Spark-Based Lakehouse
• Data Catalogue
• AI Platforms
• Enterprise Data Platforms