Data Scientist III

About the role

At Jefferson Lab, you’ll champion cutting-edge science and operational excellence while shaping the future of discovery. Join us and make your mark – where excellence meets purpose, and great minds truly matter. The Jefferson Lab campus is located in southeastern Virginia amidst a vibrant and growing technology community.

Responsibilities

• Lead high impact, data driven research that supports mission critical programs across environmental science, climate modeling, nuclear physics, and energy systems.
• Design advanced statistical and machine learning solutions for complex scientific challenges, build scalable data workflows, and collaborate with interdisciplinary teams to push forward innovation in computational science.
• Develop advanced data science models including spatiotemporal statistical frameworks, deep learning architectures, generative models, and probabilistic simulations.
• Build surrogate models, reduced order models, advanced surrogate and probabilistic machine-learning models, and streaming data inference tools for large-scale scientific datasets.
• Design workflows for uncertainty quantification, extreme value analysis, statistical validation, and multi model data fusion.
• Conduct research leveraging high-performance computing, scientific data compression, and scalable analytics in support of exascale computational environments.
• Collaborate with scientists and engineers to translate complex scientific questions into robust computational or statistical solutions.
• Publish research findings in peer-reviewed journals; present work at national and international scientific meetings.
• Lead and contribute to federally funded research programs (e.g., DOE ASCR, SciDAC, BER, NP), serving as PI, Co PI, or institutional lead as required.
• Support proposal development for data-driven scientific initiatives.
• Contribute to scientific community service, including reviewing journal submissions, proposal evaluations, special issue editing, and participation in professional societies.
• Assist in organizing workshops, symposia, and technical sessions related to data science, machine learning, uncertainty quantification, and environmental modeling.
• Engage in outreach activities such as invited talks, lectures, and panel discussions.
• Mentor postdocs, graduate students, and interns on advanced machine learning, statistical modeling, and computational research topics.

Requirements

• 6 or more years in data science, applied statistics, or scientific machine learning.
• Demonstrated research contributions in one or more of the following areas: spatiotemporal modeling, deep learning and generative models, Bayesian inference and probabilistic modeling.
• Environmental or geophysical data analysis.
• Surrogate modeling and dimensionality reduction.
• Extreme-event modeling and uncertainty quantification.
• Experience working with large heterogeneous datasets and scientific computing environments.
• Experience mentoring junior researchers.
• Education above the minimum may be substituted for experience; relevant experience may not be substituted for education.

Preferred Qualifications

• 10 or more years in data science, applied statistics, or scientific machine learning.
• Experience in national laboratory environments or large-scale federal scientific programs.
• Publications in climate science, environmental modeling,