Chief Data Officer

Oak Ridge National Laboratory•Oak Ridge, TN

36d

About The Position

The Chief Data Officer (CDO) ensures Oak Ridge National Laboratory's leadership in the creation, assembly, curation, and utilization of data and data sets that are necessary for both the future of scientific discovery and for operational excellence, addressing national priorities in open science, energy innovation, national security, efficient and effective laboratory operations, and use of these data sets for responsible AI. The CDO provides the executive vision and oversight necessary to integrate enterprise data and AI strategies across ORNL, ensuring the laboratory remains a trusted steward of scientific, technical, and operational information and a national leader in AI-enabled research and innovation. ORNL generates and manages an extraordinary range of data types-from fundamental science to mission operations - including Scientific and Research Data, Operational and Institutional Data, Security, Safeguards, Classified Data, Programmatic and Collaboration Data, Knowledge, Publications, Derived Data, and Emerging Data Domains. How these diverse data types are aggregated, stored, governed, and utilized directly into ORNL's strategic priorities. The CDO, reporting directly to the Deputy for Science and Technology, will design and implement a multi-tier data strategy encompassing the full data lifecycle, curation, analytics, AI enablement, and compliance automation. This framework will connect architecture, governance, and value creation-laying the foundation for ORNL's vision and mission, including readiness for emerging missions such as fusion energy systems and autonomous science.

Requirements

Ph.D. in computer science, computational science, data science, or a data-intensive scientific discipline. A minimum of 20 years of experience working in data-intensive or computational research environments, including progressive leadership responsibility in data strategy, AI integration, or digital transformation. An equivalent combination of education and experience will also be considered.
Understanding of DOE mission spaces, data management directives, and the scientific computing ecosystem.
Demonstrated ability to lead cross-functional teams bridging science, computing, and operations, and to communicate effectively with executives, researchers, and external stakeholders.
Proven record of strategic impact in advancing data readiness, AI-enabled discovery, or enterprise-scale digital innovation.

Responsibilities

Provides strategic, executive, and thought leadership to establish and oversee a cohesive enterprise-level data strategy aligned with the ORNL mission, DOE priorities, and federal data and cybersecurity policies. The enterprise data strategy will enable the scientific and mission support organizations to accelerate scientific discovery and operational insight, and to ensure the laboratory's readiness for next-generation capabilities, including AI-augmented experimentation. This strategy will also ensure that data assets are AI-ready, FAIR (Findable, Accessible, Interoperable, Reusable), secure, and ethically managed throughout their lifecycle.
Chair an ORNL Data Governance Council to set policies for data stewardship, accessibility, ethics, and risk management, and work closely with other governance councils focused on AI (such as the Publication Ethics Council).
Partner with the Laboratory Director's Office, Chief Information Officer (CIO), and Associate Laboratory Directors (ALDs) to optimize data infrastructure, interoperability, cataloging, curation, and long-term storage architectures across mission, open, and classified environments.
Collaborate with ORNL facility, initiative and project leads advancing AI-ready and autonomous science initiatives to successfully scale practices across the laboratory. Examples include collaboration with facility leads, leaders of the AI, INTERSECT and Laboratories of the Future (LOTF) initiatives at ORNL, and ORNL leads of DOE Genesis AI initiative elements.
Partner with ORNL leaders to represent the laboratory as needed in national, international, and federal interagency or policy committees focused on advancing data strategies, including for AI-readiness.