Sr Director, Data & AI Strategy

The Sr Dir Data & AI Strategy - TEC is accountable for Tampa Electric's artificial intelligence (AI), data, and the data and AI governance capabilities. Leads a three-pillar organization: AI, Data, and Governance, helping advance the company's use of AI safely, in a governed manner, and without unnecessary delay, with success measured in O&M dollars saved, capital expenditures deferred, and new revenue created. The role sits within Technology and is accountable to the business for delivering solutions on committed timelines and service levels and for communicating clearly throughout delivery. Operates a business-driven, technology-enabled model in which the business owns the problem, the value, and adoption, and this role owns the AI and data platforms, engineering, standards, and value tracking. Repositions the Data Analytics Platform (DAP) from an end-state program into the governed data foundation that enables AI, and establishes the use-case intake, AI/data sandbox, and path-to-production capabilities that allow the business to experiment quickly while ensuring nothing reaches production ungoverned. Partners with the GUIDE (Empowering Governance while Unlocking Innovation, Data Analytics, and Enterprise AI) Decision Board and a business-chaired AI Value Council so that low-risk work proceeds understanding guardrails and governance attention is reserved for material risk and exceptions. Collaborates with Peoples Gas System (PGS) on a shared strategy and common standards on an ongoing basis.

PRIMARY DUTIES AND RESPONSIBILITIES

1. Builds and leads the AI pillar. A standing AI delivery capability (AI/ML engineering, data science, and solution architecture) that serves as the front door for business use cases. Operates the end-to-end delivery workflow: intake and qualification, prioritization and funding, prototyping in a governed AI/data sandbox, pilot and validation with real users, and release to production. Ensures no solution is released without a named accountable business owner, business-defined acceptance criteria, a completed risk review, and a funded support model. Publishes and meets service-level and communication commitments to the business.

1. Develops and executes the enterprise AI and data strategy and multi-year roadmap, aligned with business objectives, the Technology strategic plan, the TECO Responsible AI Policy, and Emera Cyber Security Framework standards. Prioritizes AI use cases by their ability to reduce O&M costs, defer capital, improve reliability and customer experience, or create new revenue. Evaluates market-leading AI technologies, including generative and agentic AI, machine learning, computer vision, and grid-edge analytics, and recommends where AI is, and is not, the right solution.

1. Leads the Data pillar, transitioning the DAP from an end-state outcome into the AI-enabling data foundation for the enterprise. Directs data platform engineering, integrations, pipelines, master data, and data quality across enterprise and operational data sources (including ADI systems), with data readiness for priority AI use cases as the primary measure of platform value. Defines the platform operating and investment strategy, resource plan, and support model, optimizing total cost of ownership and rebuilding business confidence in the platform's value.

1. Leads the Governance pillar and serves as Technology's accountable leader for AI and data before the GUIDE Decision Board and the AI Value Council. Establishes distinct AI governance and data governance, including AI risk tiers, an enterprise AI use-case registry, model risk management, standing authorizations for low-risk work, and governance by exception. Ensures solutions meet NERC CIP, CEII/BCSI, SOX/ITGC, privacy, records retention, and cybersecurity requirements, in partnership with Cybersecurity, Compliance, Legal, and Audit Services.

1. Defines, tracks, and reports AI and data value and performance, including cycle time to value, value realized (O&M, capital, and revenue), adoption, and unmanaged risk exceptions, as well as run-cost measures such as model consumption/token spend, accuracy, and drift. Coordinates business-funded development capital with Technology capital and O&M so recurring support costs are committed, not assumed, and supports documentation needed for prudence and cost recovery.

1. Partners with business units as the initial anchor business unit and scales the model to additional business units and PGS as demand is proven. Works with embedded business AI product leads on portfolio development, process redesign, and benefits realization. Leads change management and builds AI and data literacy so that solutions are adopted and embedded in how work is done.

1. Builds, develops, and retains a high-performing AI, Data, and Governance organization. Leads workforce planning, including repurposing existing DAP roles into the AI pillar, reducing reliance on contract resources over time, and recruiting specialized AI talent. Develops succession and backup coverage for critical roles.

1. Manages AI and data vendors, platform providers, and implementation partners, holding them to defined outcomes, consolidating licenses and contracts across the enterprise, and preventing duplicative or ungoverned AI and cloud spend.

QUALIFICATIONS

Education

Required: Bachelor's degree in Data Science, Computer Science, Engineering, or other related field.

Preferred: MBA or advanced degree in data science, artificial intelligence, or a related field.

Experience:

Required: 10 years of progressive experience in data, analytics, AI/ML, technology strategy, and/or enterprise transformation, including translating business strategy into scalable capabilities and measurable outcomes.

Preferred: Experience in a utility or other critical-infrastructure environment.

Familiarity with NERC CIP, CEII, SOX, and rate-regulated capital and O&M cost recovery.

Experience applying AI to operational or grid data, such as AMI, ADMS, asset and work management, drone data, or imagery data.

Supervisory Experience:

Required: 5 years of people-leadership experience. Leadership experience in complex technical or analytical environments, including leadership of AI, data engineering, or data science teams.

Knowledge/Skills/Abilities (KSA)

• Ability to deliver AI/ML and generative AI solutions from use-case intake through enterprise production, including MLOps/LLMOps, production support, and model monitoring.
• Ability to lead enterprise data platforms that enable AI, including operating models, investment strategy, total cost of ownership, and multi-year roadmap planning and establishing AI and data governance, including risk management, data quality, data stewardship, responsible AI, and controls for a regulated environment.
• Ability to lead complex, multi-year transformations with accountability for budgets, vendors, and measurable business outcomes, to include driving organizational change and advancing technology.
• Ability to explain AI and data concepts clearly to executives, business users, auditors, and regulators.
• Strong leadership, strategic and product management, and business process redesign skills.
• Ability to build collaborative relationships is willing and able to constructively challenge business and technical stakeholders to understand needs and protect value.

OUR COMPETENCIES

• Puts Safety First - Protect people and the environment every day
• Be Curious & Adapt - Welcome change and keep learning
• Create Value for Customers - Build relationships and deliver quality work
• Be Accountable & Respectful - Do what's right and follow through
• Foster Strong Teams - Build trust through teamwork and help people grow

WORKING CONDITIONS

Normal office environment.