Sr Director, Data & AI Strategy

TECO Energy•Tampa, FL

About The Position

The Sr Director, Data & AI Strategy – TEC is accountable for Tampa Electric’s artificial intelligence, data, and data and AI governance capabilities. The role leads a three-pillar organization—AI, Data, and Governance—to advance the company’s use of AI safely, with appropriate governance and without unnecessary delay. Success is measured through O&M savings, deferred capital expenditures, and new revenue. Within Technology, the role is accountable to the business for delivering solutions to committed timelines and service levels and communicating clearly throughout delivery. The position operates a business-driven, technology-enabled model: the business owns the problem, value, and adoption, while this role owns AI and data platforms, engineering, standards, and value tracking. It repositions the Data Analytics Platform (DAP) as a governed data foundation for AI and establishes use-case intake, a governed AI/data sandbox, and a path to production. It partners with the GUIDE Decision Board and a business-chaired AI Value Council, and collaborates with Peoples Gas System on shared strategy and common standards. The role develops and executes enterprise AI and data strategy and a multi-year roadmap aligned with business objectives, Technology’s strategic plan, the TECO Responsible AI Policy, and Emera Cyber Security Framework standards. It leads AI delivery, data platform engineering, and AI and data governance; measures business value and platform performance; coordinates funding and support models; and partners with business units to scale proven solutions. It also builds AI and data literacy, leads organizational change, develops a high-performing organization, and manages vendors and technology spending. The position is based in a normal office environment. Responding to storms and participating in emergency and disaster response and recovery activities are conditions of employment. The company states that proper compensation will be made under its rules and procedures.

Requirements

Bachelor’s degree in Data Science, Computer Science, Engineering, or another related field.
At least 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.
At least 5 years of people-leadership experience.
Leadership experience in complex technical or analytical environments, including leadership of AI, data engineering, or data science teams.
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.
Ability to establish 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, drive organizational change, and advance 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 and constructively challenge business and technical stakeholders to understand needs and protect value.
Ability to respond to storms and participate in emergency and disaster response and recovery activities as a condition of employment.

Nice To Haves

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

Responsibilities

Build and lead the AI pillar, including an AI/ML engineering, data science, and solution architecture capability serving as the front door for business use cases.
Operate the end-to-end AI delivery workflow, from intake, qualification, prioritization, and funding through sandbox prototyping, pilot validation, and production release.
Ensure each production solution has a named accountable business owner, business-defined acceptance criteria, a completed risk review, and a funded support model.
Publish and meet service-level and communication commitments to the business.
Develop and execute the enterprise AI and data strategy and multi-year roadmap aligned with business objectives, Technology plans, responsible AI policy, and cybersecurity standards.
Prioritize AI use cases based on potential to reduce O&M costs, defer capital, improve reliability and customer experience, or create revenue; evaluate AI technologies and recommend where AI is and is not appropriate.
Lead the Data pillar and transition the DAP into an AI-enabling enterprise data foundation.
Direct data platform engineering, integrations, pipelines, master data, and data quality across enterprise and operational data sources, including ADI systems.
Define the data platform operating and investment strategy, resource plan, and support model, optimizing total cost of ownership and platform value.
Lead the Governance pillar and represent Technology as its accountable AI and data leader before the GUIDE Decision Board and AI Value Council.
Establish distinct AI 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.
Ensure solutions meet NERC CIP, CEII/BCSI, SOX/ITGC, privacy, records retention, and cybersecurity requirements in partnership with relevant functions.
Define, track, and report AI and data value and performance, including cycle time to value, realized O&M/capital/revenue value, adoption, unmanaged risk exceptions, model consumption and token spend, accuracy, and drift.
Coordinate business-funded development capital with Technology capital and O&M, ensure recurring support costs are committed, and support documentation for prudence and cost recovery.
Partner with business units, initially using an anchor business unit, and scale the model to additional business units and PGS as demand is proven.
Work with embedded business AI product leads on portfolio development, process redesign, and benefits realization.
Lead change management and build AI and data literacy to support adoption and embed solutions in work practices.
Build, develop, and retain a high-performing AI, Data, and Governance organization; lead workforce planning, succession planning, and backup coverage for critical roles.
Repurpose existing DAP roles into the AI pillar, reduce reliance on contract resources over time, and recruit specialized AI talent.
Manage AI and data vendors, platform providers, and implementation partners against defined outcomes; consolidate licenses and contracts and prevent duplicative or ungoverned AI and cloud spending.
Participate in storm, emergency, and disaster response and recovery activities as required to maintain customer service.