Energy Resilience Data Scientist

Job Description

We have an opening for an Energy Resilience Data Scientist
. This role sits at the intersection of data science, energy systems, and critical infrastructure resilience, transforming complex, multi-source data into actionable insights for resilience planning and risk assessment.

In this role, you will develop data‑driven and applied machine learning methods to assess, model, and improve the resilience of energy systems. You will combine applied research and critical thinking to understand complex, heterogeneous datasets, build predictive and decision‑support models, and communicate results to multidisciplinary engineering teams and program sponsors. You will support LLNL’s Cyber and Infrastructure Resilience (CIR) program’s growing research portfolio in electric grid infrastructure within the Computational Engineering Division.

This position will be filled at either SES.
2 or SES.
3 level based on knowledge and related experience as assessed by the responsibilities listed below. Higher level qualifications will be assigned when hired at the SES.
3 level.

• Develop and apply data science and machine learning methods to characterize and assess energy infrastructure resilience under a range of disruptions, including natural hazards, extreme weather, infrastructure failures, and other stressors.
• Build, train, and evaluate data‑driven models, selecting appropriate architectures and metrics for the mission problem.
• Integrate heterogeneous data sources into reproducible analytics pipelines.
• Collaborate with multidisciplinary engineering teams to iterate on model design, evaluation, and application, and deliver validated results.
• Produce clear technical documentation, reports, and briefings.
• Publish research results in peer‑reviewed journals, conference proceedings, and laboratory reports, as appropriate.
• Routinely interact with technical contacts at sponsor and partner organizations.
• Support the growth of the laboratory’s energy resilience research portfolio through collaboration across programs and disciplines.
• Perform other duties as assigned.

• Provide technical leadership and guidance to multiple diverse, technical teams of LLNL scientists and engineers to operationalize research and development advancements for national security programs.
• Independently determine technical objectives and criteria to satisfy project deliverables and execute the appropriate technical approaches.
• Serve as the technical point of contact for program managers at sponsor and partner organizations, providing advanced-level knowledge, recommendations on methodologies, and influence to fulfill deliverables and meet sponsor needs.

• Ability to secure and maintain a U.S. DOE Q‑level security clearance requiring U.S. citizenship.
• Master’s degree in data science, applied statistics, computer science, engineering, or a related field, or equivalent combination of education and experience.
• Comprehensive knowledge and experience with one or more of the following computational disciplines: applied machine learning, statistical modeling, risk analysis, data analytics.
• Comprehensive experience in applied machine learning, including developing and evaluating models using PyTorch or Tensor Flow.
• Strong Python programming skills, including experience with the scientific Python stack for data science (Num Py, Sci Py, Matplotlib). Demonstrated experience performing geospatial analytics in Python using Geo Pandas or equivalent geospatial tools and libraries.
• Comprehensive knowledge and experience in developing data‑driven models and/or frameworks to characterize and assess infrastructure systems and/or threats and risks to these systems.
• Proficient verbal and written communication skills necessary to effectively collaborate in a multi‑disciplinary team delivering results on schedule and adapting to evolving requirements.
• Demonstrated analytical, problem‑solving, and decision‑making skills to effectively develop creative solutions to moderately complex problems.
• Ability to travel off‑site for sponsor and customer interactions.

• Ph.D. in data…