Principal Data Analytics & AI Strategist

Job Description

Principal Data & Analytics Strategist | Mayo Clinic

The Tone:
This is a principal-level individual contributor role at Mayo Clinic. Mayo Clinic uses data, analytics, and AI to address complex healthcare problems and advance patient care. This role is crucial for shaping how the enterprise’s data, analytics, and AI strategy translates into scalable solutions and consistently delivers sustained, measurable value. The strategist will serve as a technical authority and enterprise-level thought leader, connecting technical decisions to strategic intent.

The TL;DR
• Role: Full Time
• Mission: This role translates enterprise data, analytics, and AI strategy into scalable solution patterns and delivery models that consistently deliver sustained, measurable value.
• Tech Stack: Google, Azure

What You’ll Actually Do
• Define Strategy: Define and translate enterprise data and AI strategy into scalable solution patterns, architectural guardrails, and delivery models.
• Ensure Alignment: Ensure system-level technical decisions align with broader enterprise data and analytics strategy, governance, and investment goals.
• Guide Lifecycle: Guide the full solution lifecycle from opportunity framing through solution design guidance and delivery oversight across various domains and portfolios.
• Establish Patterns: Establish and promote reference architectures, preferred patterns, and decision frameworks, including hands-on exploration and prototyping where early technical validation is critical.
• Partner and Influence: Partner with senior leaders and cross-functional teams (data engineering, analytics/BI, AI/ML, platform, security, governance) to align technical direction and facilitate enterprise-wide decision-making.

The Must-Haves
• Background: Bachelor’s degree in computer science, information systems, engineering, mathematics, statistics, data science, or a related field.
• Experience: Extensive (15-20+ years) experience in enterprise data, analytics, and/or AI strategy, architecture, consulting, or product/program delivery. Demonstrated experience defining enterprise-level strategy and translating it into executable roadmaps, capability models, and delivery guardrails across multiple portfolios. Experience operating in regulated environments (e.g., healthcare, research, financial services), with familiarity with privacy, compliance, governance, and responsible AI expectations.
• Skills: Strong working knowledge of modern data and analytics concepts (data products, data platforms, pipelines, BI/visualization, governance, metadata, quality, privacy/security fundamentals, and observability). Proven ability to communicate complex technical implementation concepts to executive leadership, including architecture tradeoffs, investment options, risk, and sequencing; produces clear, decision-ready materials. Demonstrated facilitation skills for executive and technical audiences (workshops, strategic reviews, governance forums) and strong written communication skills. Experience influencing across senior stakeholders and cross-functional teams (engineering, analytics, AI/ML, security, privacy, architecture, governance) to drive alignment and decisions in ambiguous environments.
• Bonus: Master’s degree or PhD in a related field (e.g., computer science, data science, business analytics, healthcare informatics, or MBA). Certification in one or more major cloud platforms (Google, Azure, etc). Experience establishing or evolving enterprise data operating models (e.g., data product operating model, platform governance, domain engagement, stewardship models) and measuring adoption/maturity over time. Experience developing and/or governing enterprise AI strategy, including responsible AI controls, model risk management, and GenAI/agentic patterns (grounding, evaluation, monitoring). Experience with cloud data platforms and modern architecture patterns (lakehouse/warehouse, streaming/eventing, semantic layers/metrics, MDM/reference data), including cost/value tradeoffs. Demonstrated experience building reusable playbooks, reference architectures, templates, and standards that scale delivery across multiple teams. Prior experience in working through large-scale AI transformations for organizations including creation of vector stores, knowledge graphs, MCP servers and getting an organization’s data teams AI-ready.