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: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.
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 ExchangeEducation above the minimum may be substituted for experience. Relevant experience may not be substituted for education.
Licenses and CertificationsKnowledge, 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.
Additional Licenses and Certifications 
