Energy Resilience Data Scientist

Benefits

Parental leave

Not verified

Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

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Childcare support

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Learning budget

Offered From the posting source checked Jun 20, 2026

Verification

Not verified

Salary

$146K-$223K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

65% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Energy Resilience Data Scientist Livermore, CA, United States 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

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