Research Scientist, Spatial Statistics

Requisition Id 15124

Overview:

We are seeking a Research Scientist to join the Spatial Statistics Group within the Geospatial and Human Security Division. This group develops and applies cutting-edge statistical and mathematical methods to solve complex spatio-temporal problems at scale, supporting operational decision-making under uncertainty. Our mission spans diverse applications in national security, critical infrastructure resilience, and humanitarian challenges, with a particular emphasis on problems where data may be vast, sparse, noisy, or incomplete.

Key methodological areas of interest include Bayesian data analysis, nonparametric statistics, functional data analysis, spatio-temporal statistics, and machine learning/artificial intelligence. Many of our projects involve dynamic processes evolving in space and time, requiring novel approaches to modeling uncertainty and making predictions in real-world, operational contexts. We operate at the intersection of theory and practice, often advancing analytical methods alongside their implementation in scalable systems supported by high performance computing (HPC) and automated workflows.

Example application domains include dynamic population estimation, critical infrastructure monitoring, transportation analytics, energy security, humanitarian assistance, and threat detection. The group's work frequently supports situational awareness and decision-making processes under uncertainty in real operational settings. The ability to design, implement, and effectively communicate complex analytical frameworks-both to technical peers and non-technical collaborators-is essential for success in this role.

As a Research Scientist, you will have the opportunity to lead and contribute to impactful research, publish in peer-reviewed journals and conferences, mentor junior staff, and build collaborative networks across ORNL, DOE, and the broader scientific community. You will help shape the group's technical strategy while ensuring our efforts meet the evolving needs of sponsors and advance the state of the art.

Major Duties/Responsibilities:

• Lead and contribute to the development of innovative methods in spatial statistics, with particular emphasis on scalable workflows in high-performance computing (HPC) and applied spatio-temporal modeling.
• Conduct independent and collaborative R&D, producing impactful results as evidenced by innovative solutions delivered to sponsors, successful research proposals, technical presentations, peer-reviewed publications, and active engagement with the professional community.
• Translate complex mathematical and statistical approaches into actionable solutions in real-world operational settings, supporting situational awareness and decision-making under uncertainty.
• Engage with interdisciplinary teams across ORNL, DOE, and external partners to identify emerging challenges and drive technical solutions in areas such as population dynamics, critical infrastructure monitoring, and dynamic network analysis.
• Contribute to a culture of scientific excellence, rigor, and innovation through mentoring, collaboration, and professional service.
• Deliver on ORNL's mission by aligning your work and leadership with our core values of Impact, Integrity, Teamwork, Safety, and Service.

Basic Qualifications:

• A Ph.D. in statistics, applied mathematics, computer science, or a related field, with at least two (2) years of relevant research experience; an equivalent combination of education and experience may be considered.
• Demonstrated experience working with spatial and spatiotemporal data, including structured and unstructured sources such as remote sensing, text, and time series data.
• Proficiency in two or more of the following areas: applied statistics, spatial statistics, probabilistic modeling (e.g., Bayesian methods), spatio-temporal modeling, or machine/deep learning.
• Experience developing proof-of-concept analytical workflows or models using R, Python, or similar programming languages.

Preferred Qualifications:

• Demonstrated ability to successfully identify, shape, and deliver complex technical projects, from initial concept through implementation and transition to operations.
• Excellent interpersonal skills and a strong commitment to collaborative, interdisciplinary teamwork.
• Strong written and verbal communication skills, including the ability to convey technical concepts to both expert and non-technical audiences.
• Ability to thrive in a fast-paced research environment, set priorities effectively, manage multiple tasks within deadlines, and adapt to evolving project needs.
• Self-motivated, proactive, and capable of working both independently and as part of creative, multidisciplinary teams.
• Experience developing quantitative frameworks for decision-making under uncertainty, including spatio-temporal modeling, uncertainty quantification, and effective communication of results to non-technical partners.
• Familiarity with application domains such as mobility analytics, energy and infrastructure security, urban systems, remote sensing, transportation networks, and spatio-temporal population dynamics.
• Knowledge of machine learning (ML) and AI techniques relevant to spatio-temporal modeling, including methods for uncertainty quantification, interpretability, and scalability in operational settings.
• Experience developing and deploying machine learning solutions in high-performance computing (HPC) environments.

Special Requirements:

• Visa sponsorship: Visa sponsorship is not available for this position.
• SCI Clearance: This position requires the ability to obtain and maintain a Secret Compartmented Information (SCI) clearance from the Department of Energy. As such, this position is a Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing a pre-placement drug test and participation in an ongoing random drug testing program. In addition, due the SCI, you may also be subject to random polygraph testing.

Benefits at ORNL:

ORNL offers competitive pay and benefits programs to attract and retain dedicated people! The laboratory offers many employee benefits, including medical and retirement plans and flexible work hours, to help you and your family live happy and healthy. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also provided for convenience.

Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov.

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.