J-1 Visa Data Scientist Jobs
Data Scientist roles in the United States are accessible to international professionals through J-1 visa sponsorship under the Research Scholar, Trainee, or Intern program categories, depending on your career stage. A U.S. Department of State-designated sponsor organization issues your DS-2019, while your hiring company serves as the host.
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The Data and AI Systems Research Section within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance computing (HPC), with an emphasis on developing intelligent systems that can accelerate large-scale scientific research on leadership-class supercomputers.
The successful candidate will contribute to research efforts supported by the U.S. Department of Energy Office of Science, including the Advanced Scientific Computing Research (ASCR) program and the Genesis initiative. These programs focus on integrating AI directly into scientific workflows to enable autonomous, data-driven discovery in areas such as fusion energy, materials science, climate science, and nuclear energy.
As part of ORNL’s interdisciplinary research environment, you will work alongside scientists, engineers, and computational researchers while leveraging world-class computing resources, including Frontier, the world’s first exascale supercomputer. The role includes developing and advancing open-source software for large-scale hyperparameter optimization (HPO), neural architecture search (NAS), and Bayesian optimization on distributed HPC systems.
Research activities will address key challenges in AI for science, including surrogate modeling, uncertainty quantification, and multi-fidelity optimization for complex simulation workflows. This position offers an opportunity to contribute to cutting-edge AI and HPC research while supporting DOE’s broader mission to advance scientific innovation through computational science.
The appointment length is 2 years with the possibility of extension, subject to performance and availability of funding.
Major Duties and Responsibilities:
- Conduct research and development in scalable AI/ML methods for scientific computing and high-performance computing environments.
- Develop and evaluate optimization techniques for machine learning workflows, including approaches for model tuning, automated model design, and adaptive search strategies.
- Contribute to research in uncertainty quantification, surrogate modeling, and other methods that improve the robustness and reliability of AI-driven scientific applications.
- Design and implement distributed and parallel approaches that efficiently leverage large-scale computing resources, including heterogeneous CPU/GPU systems, along with the possibility of working with Quantum computing.
- Collaborate with interdisciplinary research teams to integrate AI/ML capabilities into scientific simulation, data analysis, and computational workflows.
- Contribute to the development and maintenance of open-source software, including testing, documentation, and user support activities.
- Work closely with researchers and domain scientists to communicate results, define research directions, and support collaborative projects.
- Publish research findings in peer-reviewed journals and present work at scientific workshops and conferences.
- Design and implement scalable AI/ML optimization algorithms for hyperparameter optimization and neural architecture search, targeting scientific machine learning models running on leadership-class HPC systems.
- Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.
Basic Qualifications:
- A PhD in Computer Science, Applied Mathematics, Computational Science, Data Science, or a related discipline completed within the last three years.
- An excellent record of productive and creative research as demonstrated by publications in top peer-reviewed journals and conferences.
- Demonstrated experience with machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and hyperparameter optimization or AutoML techniques.
- Proficiency in Python and familiarity with software engineering best practices (version control, testing, documentation).
- Experience with HPC environments and parallel/distributed computing.
- Strong problem-solving and communication skills, with the ability to work collaboratively in a team setting.
Preferred Qualifications:
- Experience with multi-fidelity optimization, neural architecture search, or large-scale AutoML systems.
- Familiarity with surrogate modeling, physics-informed neural networks, or uncertainty quantification for scientific applications.
- Prior exposure to DOE workflows, national laboratory environments, or large-scale simulation codes.
- Experience contributing to open-source scientific software projects.
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.
Overview:
The Data and AI Systems Research Section within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance computing (HPC), with an emphasis on developing intelligent systems that can accelerate large-scale scientific research on leadership-class supercomputers.
The successful candidate will contribute to research efforts supported by the U.S. Department of Energy Office of Science, including the Advanced Scientific Computing Research (ASCR) program and the Genesis initiative. These programs focus on integrating AI directly into scientific workflows to enable autonomous, data-driven discovery in areas such as fusion energy, materials science, climate science, and nuclear energy.
As part of ORNL’s interdisciplinary research environment, you will work alongside scientists, engineers, and computational researchers while leveraging world-class computing resources, including Frontier, the world’s first exascale supercomputer. The role includes developing and advancing open-source software for large-scale hyperparameter optimization (HPO), neural architecture search (NAS), and Bayesian optimization on distributed HPC systems.
Research activities will address key challenges in AI for science, including surrogate modeling, uncertainty quantification, and multi-fidelity optimization for complex simulation workflows. This position offers an opportunity to contribute to cutting-edge AI and HPC research while supporting DOE’s broader mission to advance scientific innovation through computational science.
The appointment length is 2 years with the possibility of extension, subject to performance and availability of funding.
Major Duties and Responsibilities:
- Conduct research and development in scalable AI/ML methods for scientific computing and high-performance computing environments.
- Develop and evaluate optimization techniques for machine learning workflows, including approaches for model tuning, automated model design, and adaptive search strategies.
- Contribute to research in uncertainty quantification, surrogate modeling, and other methods that improve the robustness and reliability of AI-driven scientific applications.
- Design and implement distributed and parallel approaches that efficiently leverage large-scale computing resources, including heterogeneous CPU/GPU systems, along with the possibility of working with Quantum computing.
- Collaborate with interdisciplinary research teams to integrate AI/ML capabilities into scientific simulation, data analysis, and computational workflows.
- Contribute to the development and maintenance of open-source software, including testing, documentation, and user support activities.
- Work closely with researchers and domain scientists to communicate results, define research directions, and support collaborative projects.
- Publish research findings in peer-reviewed journals and present work at scientific workshops and conferences.
- Design and implement scalable AI/ML optimization algorithms for hyperparameter optimization and neural architecture search, targeting scientific machine learning models running on leadership-class HPC systems.
- Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.
Basic Qualifications:
- A PhD in Computer Science, Applied Mathematics, Computational Science, Data Science, or a related discipline completed within the last three years.
- An excellent record of productive and creative research as demonstrated by publications in top peer-reviewed journals and conferences.
- Demonstrated experience with machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and hyperparameter optimization or AutoML techniques.
- Proficiency in Python and familiarity with software engineering best practices (version control, testing, documentation).
- Experience with HPC environments and parallel/distributed computing.
- Strong problem-solving and communication skills, with the ability to work collaboratively in a team setting.
Preferred Qualifications:
- Experience with multi-fidelity optimization, neural architecture search, or large-scale AutoML systems.
- Familiarity with surrogate modeling, physics-informed neural networks, or uncertainty quantification for scientific applications.
- Prior exposure to DOE workflows, national laboratory environments, or large-scale simulation codes.
- Experience contributing to open-source scientific software projects.
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.
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Get Access To All JobsTips for Finding J-1 Visa Sponsorship as a Data Scientist
Match your J-1 category to your career stage
Current students enrolled in a degree program typically qualify for the Intern category. Professionals with a relevant degree and prior data science work experience should target the Trainee category, which allows up to 18 months of supervised training in the United States.
Frame your training plan around measurable objectives
Designated sponsors require a detailed training plan before issuing your DS-2019. Structure yours around specific data science deliverables: model development milestones, tools you'll gain proficiency in, and reporting relationships. Vague plans stall approvals.
Check whether the host employer holds an existing sponsor relationship
Some research institutes and universities already have agreements with sponsors like IIE or Cultural Vistas. Ask hiring managers directly whether they've hosted J-1 visa Trainees or Research Scholars before, because an established relationship shortens your path to a certified DS-2019.
Find host employers open to J-1 arrangements on Migrate Mate
Use Migrate Mate to identify U.S. employers actively hiring Data Scientists in contexts aligned with J-1 sponsorship. Filter by role and sector so you're targeting organizations, such as research labs or analytics teams, that routinely work with exchange visitors.
Verify your SOC code before any sponsor conversation
Sponsors and host employers use the Standard Occupational Classification system to categorize your role. Pull the Data Scientist profile from O*NET and confirm your job duties align with that code. Mismatched duties are a common reason training plan submissions get returned.
Clarify home residency requirements early in your offer negotiation
Depending on your funding source or country of nationality, you may be subject to the two-year home residency requirement after your J-1 ends. Resolve this before signing an offer letter, because it directly affects any future H-1B visa or green card timeline your employer might expect.
Data Scientist J-1 Visa: Frequently Asked Questions
Which J-1 program category fits a Data Scientist role?
It depends on your career stage. If you're currently enrolled in a graduate program and the work is part of your curriculum, the Intern category applies. If you've already completed your degree and have professional data science experience, the Trainee category is typically the right fit, covering supervised training for up to 18 months. Researchers affiliated with universities or institutes may qualify under the Research Scholar category instead.
Who actually sponsors my J-1 visa as a Data Scientist?
Your visa sponsor is a U.S. Department of State-designated organization, not your employer. Organizations like IIE, Cultural Vistas, and CIEE issue the DS-2019 form that establishes your exchange visitor status. Your hiring company is the host, not the sponsor. The designated sponsor monitors your program compliance and is legally responsible for your exchange visitor program, while your employer provides the training environment.
Can I work on proprietary machine learning models as a J-1 Trainee?
Yes, as long as the work aligns with the approved training plan your sponsor reviews. The training plan must describe specific learning objectives, not just production deliverables. If your role shifts significantly toward building commercial products with no clear skills-development component, you risk falling outside what a Trainee program is designed to cover. Keep your supervisor aligned with the plan's stated objectives throughout the placement.
How do I find U.S. employers willing to host a J-1 Data Scientist?
Use Migrate Mate to search Data Scientist roles at U.S. employers in sectors that commonly work with exchange visitors, such as research institutions, analytics consultancies, and technology companies with established international talent programs. From there, ask directly during the interview process whether the organization has previously hosted J-1 Trainees or Research Scholars, since an existing relationship with a designated sponsor significantly streamlines your DS-2019 issuance.
Does the two-year home residency requirement apply to Data Scientists on J-1?
It can. The two-year home residency requirement applies if your J-1 program was funded by your home country's government or the U.S. government, or if your field appears on your home country's skills list. This requirement means you must return home for two years before changing to most other visa statuses, including H-1B. Determine whether it applies to you before accepting a host employer's offer, because it affects your long-term visa options.