Title:  Data Scientist III

• 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.

• 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:o Spatiotemporal modelingo Deep learning and generative modelso Bayesian inference and probabilistic modeling.
• Required: Environmental or geophysical data analysis Surrogate modeling and dimensionality reduction Extreme-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.

• 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.

• 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 campus is located in southeastern Virginia amidst a vibrant and growing technology community.  

A career at Jefferson Lab is more than a job. You will be part of “big science” and work alongside top scientists and engineers from around the world unlocking the secrets of our visible universe. Managed by SURATech, LLC, Thomas Jefferson National Accelerator Facility is entering an exciting period of mission growth and is seeking new team members ready to apply their skills and passion to have an impact. You could call it work, or you could call it a mission. We call it a challenge. We do things that will change the world. 
 
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 recruiting@jlab.org or contact Human Resources by calling (757) 269-7100 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.