Data Scientist III

Thomas Jefferson National Accelerator Facility (Jefferson Lab); Newport News (VA); On-site; USD 118,000 - 187,000; Full time

Benefits offered by this job

Medical, Dental, and Vision care plans

401(k) Plan with Lab contribution

Flexible work arrangements

Tuition assistance and professional开发

Job summary

Jefferson Lab seeks a senior data scientist to lead high-impact research across environmental science, climate modeling, nuclear physics, and energy systems. You will design advanced statistical and ML solutions, build scalable data workflows, and collaborate with interdisciplinary teams to advance computational science.

Leading federally funded programs as PI or institutional lead, you will publish findings, mentor postdocs and researchers, and contribute to national priorities and exascale

Qualifications

• Advanced knowledge of statistical modeling, machine learning, generative modeling, and probabilistic simulation.
• Strong mathematical foundations in multivariate analysis, dimension reduction, clustering, and extreme value theory.
• Ability to develop scalable ML/AI workflows for scientific applications.
• Strong communication skills and ability to collaborate across disciplinary teams.
• Ability to design, validate, and interpret complex scientific models.
• Familiarity with scientific Python, HPC environments, and data compression frameworks.
• Ability to support mission-driven research and tight federal deliverables.

Responsibilities

• Lead data-driven research 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.
• 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.

Skills

Spatiotemporal modeling

Deep learning

Generative modeling

Bayesian inference

Probabilistic modeling

Uncertainty quantification

Statistical modeling

HPC environments

Scientific Python

Collaboration across disciplines

Education

Bachelor's degree in statistics, applied mathematics, computer science, data science, computational science, or related field

Ph.D. in statistics, applied mathematics, computer science, data science, computational science, or related field

Tools

Python

HPC environments

Data compression frameworks

Job description

At Jefferson Lab,you’llchampioncutting-edgescience and operational excellence while shaping the future of discovery. Join us and make your mark - where excellence meets purpose, andgreat mindstrulymatter.

The good-faith pay range for this role is $118,400 - $ 186,850 per year. Actual compensation may vary and may be above the posted range based on factors such as a candidate's skills, experience, education, certifications, and work location.

Company-provided relocation assistance will be offered for this position.

What your job will be like:

You will lead high impact, data driven research that supports mission critical programs across environmental science, climate modeling, nuclear physics, and energy systems. You will 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. Your work will directly support national research priorities, scientific discovery, and resilience-focused modeling efforts.

In this job you will:

• 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.

Additional Responsibilities

• 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.

Lead - Supervisory - Management

• Mentor postdocs, graduate students, and interns on advanced machine learning, statistical modeling, and computational research topics.

Experience

• Required: 6 or more years in data science, applied statistics, or scientific machine learning.
• Required: Demonstrated research contributions in one or more of the following areas:oSpatiotemporal modelingoDeep learning and generative modelsoBayesian inference and probabilistic modeling.
• Required: Environmental or geophysical data analysis Surrogate modeling and dimensionality reductionExtreme-event modeling and uncertainty quantification
• Required: Experience working with large heterogeneous datasets and scientific computing environments.
• Required: Experience mentoring junior researchers.
• Preferred: 10 or more years in data science, applied statistics, or scientific machine learning.
• Preferred: Experience in national laboratory environments or large-scale federal scientific programs.
• Preferred: Publications in climate science, environmental modeling, applied mathematics, machine learning, or nuclear physics.
• Preferred: Familiarity with DOE research programs and federal funding mechanisms.

Education

• Required: Bachelor's Degree Statistics, Applied Mathematics, Computer Science, Data Science, Computational Science, or a related field.
• Preferred: Ph.D. Statistics, Applied Mathematics, Computer Science, Data Science, Computational Science, or a related field.

Experience and Education Exchange

Education above the minimum may be substituted for experience. Relevant experience may not be substituted for education.

Knowledge, Skills, and Abilities

• Advanced knowledge of statistical modeling, machine learning, generative modeling, and probabilistic simulation.
• Strong mathematical foundations in multivariate analysis, dimension reduction, clustering, and extreme value theory.
• Ability to develop scalable ML/AI workflows for scientific applications.
• Strong communication skills and ability to collaborate across disciplinary teams.
• Ability to design, validate, and interpret complex scientific models.
• Familiarity with scientific Python, HPC environments, and data compression frameworks.
• Ability to support mission-driven research and tight federal deliverables.

About Jefferson Lab

Join a community with a common purpose of solving the most challenging scientific and engineering problems of our time. The Jefferson Lab campusis located insoutheasternVirginiaamidst a vibrant and growing technology community.

Total Rewards at Jefferson Lab

At Jefferson Lab, we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Our benefits program includes, but is not limited to:

• Medical, Dental, and Vision Care Plans
• Flexible Spending Accounts
• Paid Time-off and Leave Programs (Paid Parental, vacation, holidays, and sick leave)
• 401(k) Plan - 9% Lab Contribution; 100% vested
• Flexible Work Arrangements (Remote & Alternate Work Schedules available)
• Tuition Assistance, Training and Professional Development Programs
• Live near the waterways of the Chesapeake Bay region with access to nearby beaches, mountains, and all major metropolitan centers on the East Coast

SURATech, LLC manages and operates the Thomas Jefferson National Accelerator Facility (Jefferson Lab). SURATech is an Equal Opportunity Employer. SURATech is committed to providing reasonable accommodation for people with disabilities (unless doing so will result in an undue hardship). If you need a reasonable accommodation for any part of the employment process, please send an e-mail to [e-mail] or contact Human Resources by calling [phone] and selecting option 1 between 8 am - 5 pm EST to provide the nature of your request.

Employment with SURATech is conditional upon DOE approval if at any time during your employment you are participating in a Foreign Government Talent Recruitment Program or Affiliated activity. Generally, such programs/activities include any foreign-state-sponsored attempt to acquire U.S.-funded scientific research through programs run or funded by the government that target scientists, engineers, students, academics, researchers, and entrepreneurs of all nationalities working or educated in the United States. This includes positions or appointments, both domestic and foreign, titled academic, professional, or institutional appointments whether or not remuneration is received and whether full-time, part-time or voluntary.