Energy Resilience Data Scientist

Job description

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 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. Additional qualifications at the SES.3 level PhD in data science, applied statistics, computer science, engineering or a related field, or the equivalent combination of education and related experience. Advanced level knowledge in one or more of the following areas: infrastructure systems, energy systems, or other related discipline. Advanced knowledge and experience with one or more of the following computational disciplines: applied machine learning, statistical modeling, risk analysis, data analytics. Significant experience and advanced knowledge in developing data-driven models and/or frameworks to characterize and assess energy systems and/or threats and risks to these systems. Significant leadership skills and experience and demonstrated ability to exercise independent judgment to effectively manage diverse technical teams in executing projects. Ability to independently develop and execute complex analyses and to prepare and finalize tailored reports. Qualifications We Desire Experience with energy systems, grid operations, infrastructure resilience, risk analysis, hazard impacts, or interdependency modeling. Experience with analyzing disruptions resulting from complex threat scenarios. Comprehensive knowledge and broad experience building models and running simulations using Python. Broad experience in managing multiple concurrent projects. Pay Range $146,340 - $222,564 Annually $146,340 - $185,544 Annually for the SES.2 level $175,530 - $222,564 Annually for the SES.3 level This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. Additional Information #LI-Hybrid Position Information This is a Career Indefinite position, open to Lab employees and external candidates. Why Lawrence Livermore National Laboratory? Included in 2026 Best Places to Work by Glassdoor! Flexible Benefits Package 401(k) Relocation Assistance Education Reimbursement Program Flexible schedules (*depending on project needs) Our values - visit https://www.llnl.gov/inclusion/our-values Security Clearance This position requires a Department of Energy (DOE) Q-level clearance. If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship. Pre-Employment Drug Test External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor. Wireless and Medical Devices Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices. If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings. How to identify fake job advertisements Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. 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