Data Strategy Lead, Digital Client Experience

Marsh McLennan

Data Strategy Lead shaping AI-ready knowledge products for Marsh’s global risk advisory business. Defining data strategy, governance, semantic models, and AI enablement across DCX.

Posted 9/3/2026full-timeNew York City • Massachusetts, New York • 🇺🇸 United StatesSenior💰 $164,000 - $327,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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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.

Highest-signal resume keywords

Data StrategyKnowledge ManagementAI EnablementData GovernanceProduct Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Product ManagementRetrieval-Augmented GenerationMetadata ManagementKnowledge GraphsEntity ResolutionData Quality FrameworksOntology DesignAI Risk ControlsData ProfilingDocument Corpora Preparation

Soft Skills

Operational Practice TranslationCross-Functional CollaborationAdaptability in AmbiguityUser Adoption FocusStakeholder Partnership

Tools & Technologies

DatabricksSpark-Based LakehouseData CatalogueAI PlatformsEnterprise 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 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 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, 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

Benefits

Comp & perks

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