Data Scientist

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States' security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are dedicated to fostering a culture that values individuals, talents, partnerships, ideas, experiences, and different perspectives, recognizing their importance to the continued success of the Laboratory's mission.

Pay Range

$117,180 - $178,392 Annually

$117,180 - $148,608 Annually for the SES.1 level

$140,700 - $178,392 Annually for the SES.2 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.

We have an opening for an entry level Data Scientistto work on projects that cover a range of systems, applications, technologies, and research in areas with critical national security interest, specifically nuclear facility operations. You will work in a dynamic, multidisciplinary team of independent/entrepreneurial computer scientists, engineers, and scientific staff who research, develop, and integrate state-of-the-art algorithms, software, hardware, and computer systems solutions to challenging research and development problems. This position is in the Global Security Computing Applications Division (GS-CAD) within the Computing Directorate, matrixed to the Global Security Directorate.

These positions will be filled at eitherlevel based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level.

You will

• Collaborate with subject matter experts (SMEs) in nuclear facility operations, safety basis, authorization basis, and criticality safety to develop and optimize RAG pipelines that provide relevant context to LLMs.
• Participate in the analysis and process for nuclear operations security governing documents (e.g., documented safety analyses, technical safety requirements, specific administrative controls) to ensure machine-ingestible formats for LLM workflows.
• Contribute to the design and implementation for data pipelines to retrieve, process, and contextualize operational and regulatory data for use in GenAI models.
• Provide solutions to problems of limited complexity for software development tools and frameworks to enhance LLM performance and usability in nuclear operations and security contexts.
• Contribute to the validation and verification of LLM outputs to ensure regulatory compliance and operational reliability.
• Under general direction assess requirements and develop limited complexity data analysis algorithms to address program and sponsor needs, particularly in the context of nuclear safety and regulatory frameworks (e.g., 10 C.F.R. Part 830, DOE-STD-3009-2014).
• Work with multidisciplinary teams to integrate RAG pipelines and LLM tooling into operational workflows, supporting innovation in regulatory compliance and operational security.
• Engage with other developers and stakeholders to share knowledge, ensure deliverables, and align technical solutions with project goals.
• Perform other duties as assigned.

Additional job responsibilities, at the SES.2 level

• Contribute to the design and implementation for advanced RAG pipelines for LLMs, including retrieval mechanisms, contextual data processing, and integration into nuclear operations workflows.
• Provide solutions to moderate complex to complex problems related to data retrieval, contextualization, and integration with LLMs, creating dynamic algorithms/software modules to address specific nuclear operations challenges.
• Utilize established methods to provide solutions for regulatory frameworks and operational requirements to optimize LLM performance and ensure compliance.
• Drive innovation in the use of LLMs for regulatory compliance, operational security, and decision-making support in nuclear facility operations.

• Ability to secure and maintain a U.S. DOE Q-level security clearance, which requires U.S. citizenship.
• Bachelor's degree in data science, computer science, mathematics, statistics, or related field, or the equivalent combination of education and related experience.
• Fundamental knowledge of scientific data analysis, statistical analysis, knowledge discovery, supervised/unsupervised learning, deep learning, natural language processing, and/or big data technologies.
• Experience developing data science algorithms with C++, Python, or R in Linux, UNIX, or Windows environments, sufficient to integrate solutions into larger applications.
• Experience with machine learning frameworks (e.g., scikit-learn, PyTorch, TensorFlow) for developing data science solutions.
• Experience or familiarity with tools and technologies for RAG pipelines, LLM optimization, or prompt engineering.
• Familiar with the nuclear regulatory requirements (e.g., 10 C.F.R. Part 830, DOE-STD-3009-2014) or willingness to learn.
• Ability to effectively handle concurrent technical tasks with conflicting priorities, to approach difficult problems with enthusiasm and creativity and to change focus when necessary.
• Sufficient interpersonal skills necessary to interact with all levels of personnel.
• Sufficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information.

Additional qualifications at the SES.2 level

• Effective analytical, problem-solving, and decision-making skills to develop creative solutions to moderately complex to complex problems.
• Broad experience with Python, scientific data analysis, knowledge discovery, supervised/unsupervised learning, deep learning, and natural language processing.
• Comprehensive experience with ML concepts such as transfer learning, distributed ML, ML operations, generative models, transformers, graph neural networks, or uncertainty quantification.
• Comprehensive experience in nuclear safety, regulation, or risk assessment.
• Work independently on research concepts in a multi-disciplinary team environment, where commitments and deadlines are important to project success.

#LI-Hybrid

Position Information

This is a Career Indefinite position, open to Lab employees and external candidates.

Why Lawrence Livermore National Laboratory?

• Included in 2025 Best Places to Work by Glassdoor!
• FlexibleBenefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Our values - visithttps://www.llnl.gov/inclusion/our-values

Security Clearance

This position requires a Department of Energy (DOE) Q-level clearance.If you are selected, wewill initiate a Federal background investigation to determine if youmeet 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 useand/or possession ofmobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area whereyou are not permitted to have a personal and/or laboratory mobile devicein your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.

Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate 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. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under "Find Your Job" of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.

To learn more about recruitment scams:https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf

Equal Employment Opportunity

We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

Reasonable Accommodation

Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.

CaliforniaPrivacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitlesjob applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.