Director, AI Data Strategy and Enablement - Remote

UnitedHealth Group•Minnetonka, MN

13h•$134,600 - $230,800•Remote

About The Position

UnitedHealth Group is building an enterprise-scale AI operating model. AI experiences require trusted, connected, contextual, interpretable, governed, and actionable data that AI systems can discover, reason over, and use appropriately across journeys and workflows. In a complex health care enterprise, this data is distributed across products, platforms, business units, legacy systems, documents, operational processes, and external partners. As Director, AI Data Strategy & Enablement, you will define and lead the strategy for how enterprise data, context, and capabilities enable next-generation digital and AI experiences for our 50+ million members. You will establish the vision, operating model, priorities, standards, and roadmap required to make enterprise data AI-ready and agent-ready. This is a highly strategic, cross-functional leadership role. You will work across Digital, Product, Technology, Data & Analytics, AI, Architecture, Operations, Compliance, Risk, and business teams to identify high-value opportunities, lead structured discovery, define future-state capabilities, and mobilize a highly matrixed organization around a common strategy. You will bring design thinking, systems thinking, structured discovery, and data strategy to ambiguous enterprise problems, translating business and experience needs into actionable data and capability strategies. You will also shape how data and context are made usable by AI agents and digital skills, including discoverability, semantics, retrieval, permissions, interoperability, governance, and observability. You will help move the organization from fragmented sources of information toward a trusted, reusable, semantic, interoperable data ecosystem that enables personalized digital experiences and scales AI safely across the enterprise.

Requirements

8+ years of progressive experience in data strategy, product strategy, digital transformation, technology strategy, AI enablement, analytics, or a related field
Proven experience leading structured discovery and strategic problem solving and translating ambiguous business or customer problems into actionable opportunities and roadmaps
Demonstrated experience defining and executing enterprise-level data or AI strategies in complex organizations
Solid understanding of modern enterprise data concepts, including data products, metadata, data quality, lineage, interoperability, APIs, semantic modeling, and governed data access
Solid understanding of GenAI and agentic AI, including how AI agents access data, retrieve context, use tools, interact with enterprise systems, and operate within governance constraints
Demonstrated ability to use design thinking, systems thinking, journey mapping, or comparable structured problem-solving approaches
Demonstrated success leading change and influencing senior stakeholders across a complex, matrixed organization without direct authority
Proven excellent executive communication, facilitation, strategic planning, and storytelling skills, with the ability to translate technical concepts into business decisions and measurable outcomes

Nice To Haves

Experience in healthcare, health insurance, financial services, or another highly regulated industry
Experience building or scaling an AI-ready or agent-ready data strategy
Experience with semantic layers, ontologies, knowledge graphs, enterprise metadata, data catalogs, retrieval architectures, or reusable data products
Experience supporting LLMs, RAG, AI assistants, copilots, or agentic workflows
Experience establishing enterprise operating models, governance structures, standards, or Centers of Excellence
Familiarity with modern cloud data platforms and architectures such as Azure, AWS, GCP, Databricks, Snowflake, or comparable technologies
Familiarity with emerging agent interaction and tool-access patterns such as MCP or comparable enterprise integration approaches

Responsibilities

Define Enterprise AI Data Strategy & Roadmap: Define the vision, principles, strategic priorities, and multi-year roadmap for AI-ready and agent-ready data aligned to enterprise AI and digital experience priorities
Identify the data, semantic, knowledge, context, access, interoperability, governance, and observability capabilities required to scale AI
Prioritize investments based on member and business value, feasibility, reuse potential, and strategic importance
Balance near-term AI use cases with foundational capabilities that enable long-term scale and reduce fragmented or duplicative investments
Establish the operating model, decision rights, and partnerships needed to execute the strategy across the enterprise
Lead Discovery, Experience Strategy & Enterprise Alignment: Lead structured discovery with product, business, clinical, operations, technology, data, and experience stakeholders to understand needs, outcomes, constraints, and root causes
Apply design thinking, journey-based discovery, systems thinking, and hypothesis-driven problem solving to identify opportunities for AI-enabled experiences
Translate ambiguous problems into clear strategic choices, capability requirements, and actionable roadmaps
Facilitate alignment across stakeholders with competing priorities, different definitions, or conflicting perspectives on data and technology
Build executive alignment and influence decisions across a complex, highly matrixed organization without direct authority
Shape the Data Foundation for Agentic AI: Define principles and requirements for agent-ready data, including trust, freshness, lineage, semantics, discoverability, permissions, interoperability, and machine usability
Shape the role of semantic models, metadata, ontologies, knowledge, data products, retrieval, and context layers in AI experiences
Partner with Architecture, Technology, Engineering, and Data leaders to establish scalable patterns for AI access to enterprise data and systems, including APIs, tools, retrieval services, and other governed access mechanisms
Ensure AI experiences are grounded in authoritative enterprise sources and business context and can operate within appropriate policy, risk, and human-escalation controls
Establish requirements for data provenance, observability, traceability, auditability, and responsible use as AI systems retrieve information, make recommendations, or take action
Drive Enterprise Standards, Change & Measurable Outcomes: Establish standards for data domains, business definitions, semantic models, metadata, schemas, APIs, and reusable data products to improve interoperability and consistency
Partner with enterprise architecture and technology leaders to influence future-state architecture and promote reusable enterprise capabilities
Lead organizational change required to adopt new data strategies, standards, operating models, and ways of working across a complex matrix
Create executive narratives and business cases that connect data strategy to member experience, business value, productivity, risk reduction, and enterprise transformation
Define and track measures of AI data readiness, reuse, quality, adoption, time-to-value, and business impact; use results and learnings to continuously refine the strategy