Energy Resilience Data Scientist

Company Description

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. 

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

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, helping transform 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. This position is in the Computational Engineering Division (CED), within the Engineering Directorate.

This position will be filled at either level based on knowledge and related experience as assessed by the responsibilities (outlined below) will be assigned if hired at the higher level.

You will

• 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.

Additional job responsibilities, at the SES.3 level 

• Provide technical leadership and guidance to multiple diverse, technical teams of LLNL scientists and engineers to operationalize research and development advancements for LLNL national security programs, while executing projects and tasks and balancing priorities of customers and partners to ensure deadlines are met.
• 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 by sharing relevant advanced level knowledge, providing opinions and recommendations on methodologies, and exerting influence as needed to fulfill deliverables and best meet sponsor needs.

Qualifications

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
• Master’s degree in data science, applied statistics, computer science, engineering or a related field, or equivalent combination of education and relevant 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 TensorFlow.
• Strong Python programming skills, including experience with the scientific Python stack for data science (NumPy, SciPy, Matplotlib). Demonstrated experience performing geospatial analytics in Python, using GeoPandas 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