Chief Data Officer

Pacific Northwest National Laboratory•Richland, WA

22h

About The Position

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus. The Earth and Biological Sciences Directorate (EBSD) leads critical research in four areas: Atmospheric, Climate & Earth Sciences, Biological Sciences, Environmental Molecular Sciences, and Global Change. Our vision is to develop a predictive understanding of biological and Earth systems in transition. We aim to understand energy and material flows within the integrated Earth system; to understand, predict, and control the response of biosystems to environmental and/or genomic changes; and to Model the Earth system from the subsurface to the atmosphere. The Environmental Molecular Sciences Division is comprised of 18 interdisciplinary research teams focused on deciphering molecular-level interactions driving biological and environmental processes across temporal and spatial scales. Through computational analysis and modeling, these findings contribute to predictive understanding of how systems respond to environmental perturbations thus enabling solutions to the nation’s energy, environmental, and human health challenges. The division also manages the Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus that accelerates the research of scientists around the world by providing access to world-class expertise, instrumentation, and computational resources. The Environmental Molecular Sciences Laboratory (EMSL) is a Department of Energy, Office of Science, User Facility sponsored by the Biological and Environmental Research (BER) program. At EMSL, our researchers advance BER’s mission to achieve a predictive understanding of complex biological, Earth, and environmental systems, collaborating on projects with researchers from academia, other government laboratories, and industry. Scientists around the world partner with us to use our world-class laboratory space, expertise, and equipment, building a research community empowered to study the role of molecular processes in controlling the functioning of biological and ecological systems across spatial and temporal scales, and to enable a predictive understanding of the living Earth system. EMSL is seeking a Chief Data Officer (CDO) to lead EMSL’s computing and scientific data management strategy and operations. The CDO is responsible for continuous development, implementation and operations of EMSL’s overall digital infrastructure vision through leadership and line management of a group of experts, including IT and software engineers, data architects, data managers, computer scientists, and cyber security professionals. The CDO will have accountability for computing operations; hardware and software acquisitions; networking; data architecture; workflow and archiving decisions; software development; and ensuring that EMSL’s data environment is capable of capturing, securing, and delivering AI‑Ready FAIR data at scale. A key accountability for the CDO will be to execute on a computing and data management strategy that reinforces EMSL’s strategic science directions for sponsors, establishing EMSL as a premier thought leader in heterogenous mid-range computing, networking, and distributed data management for advancing Department of Energy Office of Science basic science research. The CDO serves as a key member of the EMSL leadership team working closely and in constant partnership with EMSL Science Area Leads, Chief Operating Officer, and Chief Science Officer. EMSL is embarking on a transformative expansion of automated laboratory science through strategic partnerships, including a major collaboration with Ginkgo Bioworks, to build a next‑generation automated laboratory generating unprecedented volumes of high‑quality experimental data. This effort is tightly aligned with the Department of Energy’s Genesis mission, requiring sophisticated data engineering workflows, highly scalable AI/ML pipelines, and robust digital infrastructure to rapidly extract scientific insight from automated, high‑throughput systems. The CDO will play a central leadership role in ensuring that EMSL’s data ecosystems, automation workflows, and AI/ML capabilities are architected and operated to enable this mission. This position comprises predominantly executive level management responsibilities with significant span of control and/or delegated authority and responsibility. The CDO manages a multi-disciplinary organization through staff leaders and subordinate managers, interacting with and negotiating at the senior management and executive levels. Management decisions impact objectives, goals and long-term success of the organization and Laboratory. Responsibilities may involve line, sector or functional lead responsibilities as part of any compensable factor at any level. As a senior EMSL executive, the CDO will co‑shape long‑term laboratory strategy, integrating digital, data, and AI capabilities with core scientific priorities and national scientific missions. The CDO will serve as EMSL’s primary strategic voice on data, automation, AI/ML, and cyberinfrastructure in interagency and inter-laboratory forums, representing EMSL’s needs and vision to DOE sponsors and collaborative facilities. This role requires a highly collaborative and participatory leadership style that builds trust, invites diverse perspectives, and empowers managers, technical staff, and scientific partners to contribute to shared decision‑making and continuous improvement.

Requirements

BS/BA and 10 years of relevant experience -OR-
MS/MA or higher and 8 years of relevant experience

Nice To Haves

Experience with scientific data architecture, large-scale data modeling, networking and database design, and business requirements gathering analysis, ideally including technical leadership in a user facility organization interfacing with scientific users.
Previous management experience of a team of 25+ people, including experience leading other managers.
Demonstrated experience building or managing large‑scale automated laboratory data ecosystems, including high‑throughput experiment data capture, workflow orchestration, and automated data quality assurance.
Proven track record architecting and delivering AI/ML pipelines for scientific or industrial data at scale, including applications in automation‑driven environments.
Executive‑level experience leading multi‑disciplinary technical organizations, including oversight of managers, senior technical staff, and cross‑functional teams.
Demonstrated experience in large scale enterprise and data warehouse initiatives involving multiple departments and organizations, and collaboration with external partners to execute and evolve data strategy.
Strong communications skills including advocating strategies and processes around data governance and modeling across departments and senior leaders.
Deep understanding of data governance principles and FAIR data principles, ideally with experience working in an environment where data are privately embargoed prior to public release.
Knowledge of systems development, including system development life cycle, project management approaches and requirements, design and testing techniques.
Understanding of predictive modeling, data mining, visualization, machine learning and artificial intelligence, text analysis, and natural language processing.
Knowledge of data engineering, data cleansing, data analysis, data anomalies detection, and database administration.
Ability to create complex process flow diagrams or flowcharts that demonstrate business or system process and data flows, solicit feedback and gain stakeholder support through productive iteration.
Strong problem-solving and analytical skills, creativity and the ability to work with abstract concepts to create innovative and appropriate data architectural proposals and work to ensure their adoption.
Senior-level experience delivering AI-powered software products and leading teams that use AI as an integral part of the development process, including AI-assisted coding, testing, and architecture design.
Knowledge of high-performance computing (HPC) systems and hardware.
Knowledge of cloud computing.
Knowledge of common data standards, databases, and software tools/frameworks used in biology and chemistry research.
Familiarity with emerging AI agent technologies and standards (e.g. Model Context Protocol).
Demonstrated executive-level experience leading through openness, transparency, and participatory leadership, actively involving staff in decision processes, creating shared vision, and enabling teams to influence strategy, operations, and organizational culture.
Experience ensuring compliance with federal cybersecurity, privacy, and data protection requirements, ideally within a DOE or federal research environment.
Demonstrated ability to lead organizational change, modernize legacy systems, and drive adoption of new technologies and workflows.

Responsibilities

Provide participatory, inclusive leadership that actively engages EMSL staff at all levels - inviting input, fostering shared ownership of decisions, and cultivating a psychologically safe environment where cross‑disciplinary collaboration thrives.
Model transparency, clarity and openness in decision making, ensuring technical teams, scientists, and operational partners are informed, heard, and meaningfully engaged in shaping data and cyberinfrastructure strategies.
Develop and mentor leaders and managers within the organization, strengthening leadership bench depth and promoting a culture of accountability, empowerment, and continuous learning.
Ensure EMSL’s data workforce evolves to meet future automation, AI/ML, and high‑throughput scientific needs by fostering continuous skill development, talent acquisition, and strategic workforce planning.
Lead strategy development, implementation, and continuous improvement of data generation and complex scientific data engineering workflows supporting EMSL’s automated laboratory initiatives, including the Ginkgo Bioworks collaboration and DOE Genesis mission.
Oversee the design and operation of scalable AI/ML‑ready data architectures that support automated, high‑throughput experimental pipelines, including near‑real‑time data ingestion, validation, transformation, and analytics.
Partner closely with scientific and automation teams to ensure automated instruments and laboratory robotics seamlessly integrate with EMSL’s digital ecosystem, enabling reliable, high‑volume capture of AI‑Ready FAIR data.
Champion and implement advanced AI/ML workflows— including predictive modeling, automated analysis, anomaly detection, and generative model integration— to accelerate scientific discovery from large, heterogeneous datasets.
Drive organizational adoption of modern data engineering, AI, cybersecurity, and automation best practices, ensuring EMSL remains at the forefront of DOE science and technology innovation.
Establish a long-term digital modernization roadmap, aligning infrastructure investments with future scientific directions, sustainability goals, and emerging national research priorities.
Lead and oversee requirements gathering and technical planning to ensure user requirements are translated into technical specifications.
Lead data architects and data engineers to design, code, test, support, and document software applications that track data source, data movement, analysis, storage and long-term archiving.
Collaborate closely with software, IT, and data engineering teams, as well as domain scientists to ensure the development of robust and reusable data models and application programming interfaces.
Communicate effectively with EMSL science leaders and data management project team in a timely manner to escalate issues and risks appropriately.
Support educational programs for EMSL staff and champion data governance, data quality, and data integrity.
Lead and participate in the procurement process, RFIs/RFPs, and negotiations with third party vendors in crafting manageable solutions.
Communicate effectively with CDOs and CTOs of other BER facilities (such as JGI, ARM) and other DOE labs to share best practices and work together on big challenges.
Provide strategic oversight and prioritization of multi-million-dollar annual budgets, ensuring transparent fiscal stewardship, alignment with strategic goals, and effective balancing of operational reliability and innovation investments.

Benefits

Employees and their families are offered medical insurance, dental insurance, vision insurance, robust telehealth care options, several mental health benefits, free wellness coaching, health savings account, flexible spending accounts, basic life insurance, disability insurance, employee assistance program, business travel insurance, tuition assistance, relocation, backup childcare, legal benefits, supplemental parental bonding leave, surrogacy and adoption assistance, and fertility support.
Employees are automatically enrolled in our company-funded pension plan and may enroll in our 401 (k) savings plan with company match.
Employees may accrue up to 120 vacation hours per year and may receive ten paid holidays per year.

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