Sr. Director - Data, AI Strat & Adoption

Iberdrola•Boston, MA

1d•Onsite

About The Position

The Sr. Director – Data, AI Strategy & Adoption (D&AI) role serves as the enterprise leader responsible for defining and executing the enterprise data & artificial intelligence strategy, ensuring alignment with the business goals, while driving measurable adoption across the organization. This role collaborates with business stakeholders, translating data and AI capabilities into tangible business value. This position is instrumental in defining, operationalizing, and scaling AVANGRID’s Data & AI strategy across all business units. The Sr. Director – Data, AI Strategy & Adoption (D&AI) role is responsible for working with business process owners to redesign end-to-end processes to deliver measurable business value using advanced analytics, GenAI, automation, and machine learning technologies. This role is also responsible for leading a dynamic and expanding team comprised of managers, business engagement leaders, process analysts, and AI specialists. The incumbent is a senior-level leader who demonstrates a proven track record in leading AI enterprise-wide transformation, modernizing E2E business processes, and providing cross-functional leadership at scale. The Sr. Director – Data, AI Strategy & Adoption (D&AI) role reports into the Chief Data & AI Officer.

Requirements

Bachelor’s degree in computer science, Data Science, Engineering, AI/ML, Business Analytics, or related field and a minimum of fifteen (15) years of relevant experience.
An equivalent combination of education and experience may be considered.
Relevant experience includes leading enterprise Data & AI programs, including leadership experience leadership driving transformative initiatives.
Proven experience defining and executing data/AI strategies at enterprise scale.
Strong understanding of data and AI platforms, and AI/ML concepts.
Strong understanding of core utility business processes.
Demonstrated success in driving business adoption of data and analytics solutions.
Experience working in a regulatory environment e.g. energy and utilities, including regulated T&D operations, utility data domains (AMI, OMS/DMS, asset management), and reliability/system planning.
Leadership communication and presentation.
Expertise in Generative AI/LLMs, retrieval‑augmented generation (RAG), model risk management, and AI governance.
Deep understanding of process redesign/ reengineering, automation, and operational excellence.
Strong cross‑functional leadership and portfolio management; change leadership and workforce transformation.

Nice To Haves

Master’s degree in computer science, Data Science, Engineering, AI/ML, Business Analytics, or related field.
Deep understanding of regulatory environments (PUC, FERC, ISO/RTO) and their impact on AI, data, and operations.
History of building enterprise‑wide transformation programs.
Knowledge of big‑data/cloud platforms (Azure/AWS, Databricks), and modern MLOps tooling.
Professional certifications in AI, Cloud Architecture, or process redesign.

Responsibilities

Define and evolve the enterprise Data & AI strategy, long‑term roadmap, and capability maturity model aligned with business objectives.
Engage and partner with leaders across business areas to understand priorities and identify high‑impact AI use cases and value realization metrics.
Monitor industry trends and emerging technologies to inform strategic direction.
Facilitate Data & AI in enterprise strategic planning cycles, including Long‑Term Outlook (LTO) and investment planning.
Develop change management strategies to promote AI solutions and drive adoption including data products across the organization.
Promote AI and data literacy, and awareness through training and enablement programs.
Collaborate with business technical teams to scale solutions.
Serve an advisor to leadership, translating AI capabilities, risks, and impacts into clear business language.
Manage an enterprise portfolio of AI solutions.
Support enterprise Data & AI governance, including Responsible AI policies and compliance alignment in partnership with Legal, Risk, Security, and Compliance.
Drive AI literacy and change‑management programs to enable enterprise adoption and cultural transformation.
Foster a culture of innovation, experimentation and continuous improvement.
Develop talent strategies, including hiring, upskilling, and vendor/partner ecosystems.
Develop ROI for opportunities in coordination with business areas and Finance.
Reinforcement end‑to‑end accountability for the enterprise Data & AI operating model. Emphasis on governance, regulatory risk management, auditability, and long‑term talent sustainability.