Data Strategy Lead, Digital Client Experience

Marsh McLennan

. Define the DCX data and knowledge strategy and roadmap .

Posted 10/2/2026full-timeUnited StatesSenior💰 $164,000 - $327,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in data strategy, knowledge management, and AI enablement, with a strong focus on building and managing data products that drive business value. Proficient in navigating complex enterprise data environments and fostering collaboration across cross-functional teams.

Highest-signal resume keywords

Data StrategyKnowledge ManagementAI EnablementData GovernanceProduct Management

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data Product ManagementMetadata ExtractionEntity ResolutionKnowledge GraphsSemantic ModelingData Quality FrameworksRetrieval-Augmented GenerationVector SearchAI Risk ControlsDocument Corpora Preparation

Soft Skills

CollaborationProblem SolvingAdaptabilityCommunicationLeadership

Tools & Technologies

DatabricksAI PlatformsData CatalogueLakehouse ArchitecturesEnterprise Data Platforms

Industry Keywords

Financial ServicesInsuranceRisk AdvisoryProfessional ServicesData-Rich Regulated Industry

Tech Stack

Tools & technologies

Spark

About the role

Key responsibilities & impact

Define the DCX data and knowledge strategy and roadmap
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 operational insight into workflows and 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 for searching, retrieving, querying, summarizing, comparing, and evaluating knowledge assets
Contribute to priority DCX workstreams and translate ideas into practical solutions

Requirements

What you’ll need

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
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 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 sustainably

Benefits

Comp & perks

Professional development opportunities
Supportive leaders and inclusive culture
Flexible hybrid work environment
Health and welfare benefits
Tuition assistance
401K savings and other retirement programs
Employee assistance programs
Performance-based incentives may be available