Job Description

The Principal Data Analytics & AI Strategist is a principal‑level individual contributor who serves as a technical authority and enterprise‑level thought leader for data, analytics, and AI solution direction across products, platforms, and strategic problem areas. This role shapes how enterprise data, analytics and AI strategy is translated into scalable solution patterns, architectural guardrails, and delivery models that can be consistently executed across teams.

The role connects system-level technical decisions to broader enterprise data and analytics strategy, governance, and investment intent—ensuring initiatives are interoperable, governable, and positioned to deliver sustained, measurable value at scale. The Principal Data Analytics & AI Strategist operates across high ambiguity, making and documenting complex tradeoffs related to platform capabilities, data architecture, analytics and AI patterns, operating constraints, and sequencing of delivery.

Working across domains and portfolios, the Principal Data Analytics & AI Strategist influences the full solution lifecycle—from opportunity framing and options analysis through solution design guidance and delivery oversight. The role defines and socializes reference architectures, preferred patterns, and decision frameworks, supports high‑risk or high‑impact initiatives, and accelerates progress through hands‑on exploration and prototyping where early technical validation is critical.

The Principal Data Analytics & AI Strategist partners closely with senior leaders and practitioners across data engineering, analytics/BI, AI/ML, platform, security, and governance functions to align on technical direction, surface risks and dependencies early, and enable timely, enterprise‑wide decision‑making—exerting influence without direct authority to drive clarity, consistency, and execution momentum.

• Bachelor’s degree in computer science, information systems, engineering, mathematics, statistics, data science, or related field from an accredited University or College is required.
• Master’s degree or PhD in a related field (e.g., computer science, data science, business analytics, healthcare informatics, or MBA) is preferred.
• Extensive (15-20+ years) experience in enterprise data, analytics, and/or AI strategy, architecture, consulting, product/program delivery, or related discipline.
• Demonstrated experience defining enterprise-level strategy and translating it into executable roadmaps, capability models, and delivery guardrails across multiple portfolios.
• Proven ability to communicate complex technical implementation concepts to executive leadership, including architecture tradeoffs, investment options, risk, and sequencing; produces clear, decision-ready materials.
• Strong working knowledge of modern data and analytics concepts (data products, data platforms, pipelines, BI/visualization, governance, metadata, quality, privacy/security fundamentals, and observability).
• 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.
• Experience operating in regulated environments (e.g., healthcare, research, financial services), with familiarity with privacy, compliance, governance, and responsible AI expectations.
• Demonstrated facilitation skills for executive and technical audiences (workshops, strategic reviews, governance forums) and strong written communication skills.
• 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.

The ideal candidate will have prior experience in working through large scale AI transformations for organizations including creation of vector stores, Knowledge graphs, MCP servers and getting an organization data teams AI ready.