Data Strategy Lead, Digital Client ; DCX; Marsh

Data Strategy Lead, Digital Client Experience (DCX) | Marsh | Hybrid | London

We are seeking a talented individual to join the Digital Client Experience (DCX) team s role will be based in London and is a hybrid position, with a requirement to work in the office at least three days per week.

DCX (Digital Client Experience) is a Marsh Risk unit that incubates and scales client-facing digital solutions, bringing together product, design, technology, analytics, and applied AI to help clients shape risk strategy, optimize risk transfer, and proactively manage and mitigate risk. This is a hands-on, strategic role at the intersection of data strategy, product management, knowledge architecture, AI enablement, governance, and business transformation.

You will help shape how Marsh Risk transforms proprietary data, documents, analytics, and institutional expertise into reusable, well-governed, AI-ready knowledge assets. Working closely with colleagues across product, analytics, engineering, operations, and governance, you will help build the foundations for knowledge products that support client-facing solutions and more informed operational decision-making.

• Define the DCX data and knowledge strategy, setting a clear 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, ensuring focus on outcomes that matter.
• Build reusable knowledge products across areas such as client intelligence, claims intelligence, exposure data, risk engineering insight, benchmarking, policy wording, market appetite, and industry risk profiles.
• Bridge analytics and operations by embedding insight into workflows, processes, and day-to-day decision-making rather than limiting it to dashboards and reports.
• Define the knowledge layer, including core business entities, relationships, taxonomies, ontologies, semantic standards, and entity resolution across key business concepts.
• Establish governance, trust, evaluation standards, and operating models that support AI-ready knowledge products at scale.
• Support the use of AI and automation to accelerate discovery, classification, metadata extraction, data profiling, duplicate detection, and corpus preparation.
• Work with cross-functional teams to create feedback loops that continuously improve metadata, retrieval, quality, and usability of knowledge assets.
• Help define requirements for the AI-readiness of documents such as reports, presentations, contracts, broker notes, risk assessments, policy wording, claims summaries, and client deliverables.
• Partner with engineering teams to help shape tools that can search, retrieve, query, summarize, compare, and evaluate knowledge assets.
• Contribute directly to priority work streams across DCX, ensuring ideas are translated into practical, usable solutions.

• Proven experience in data strategy, data product management, enterprise data platforms, knowledge management, AI enablement, or digital product leadership.
• Demonstrated ability to bridge analytics and operations, translating data insight into operational practice and vice versa.
• Strong understanding of how AI systems use data, including retrieval-augmented generation, vector search, metadata, semantic layers, knowledge graphs, and agentic workflows.
• Experience working with complex enterprise data environments, including fragmented systems, inconsistent data quality, and mixed structured and unstructured sources.
• Demonstrated 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, with the ability to connect 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.
• Enabled teams to adopt new data, knowledge, or AI capabilities in sustainable ways.

• We help you be your best through professional development opportunities, interesting work and supportive leaders.
• We foster a vibrant and…