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 multiple openings fora Data Scientistto provide solutions for various projects. 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. These positions are in the Global Security Computing Applications Division (GS-CAD) within the Computing 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 scientists and researchers in one or more of the following areas: data intensive applications, natural language processing, graph analysis, machine learning, statistical learning, information visualization, low-level data management, data integration, data streaming, scientific data mining, data fusion, massive-scale knowledge fusion using semantic graphs, database technology, programming models for scalable parallel computing, application performance modeling and analysis, scalable tool development, novel architectures (e.g., FPGAs, GPUs and embedded systems), and HPC architecture simulation and evaluation.
• Partner with LLNL scientists and application developers to bring research results to practical use in LLNL programs.
• Assess the requirements for data sciences research from LLNL programs and external government sponsors.
• Contribute to the development of data analysis algorithms to address program and sponsor data sciences requirements.
• Engage with developers frequently to share relevant knowledge, opinions, and recommendations, working to fulfill deliverables as a team.
• Contribute to technical solutions, participate as a member of a multidisciplinary team to analyze sponsor requirements and designs, and implement software and perform analyses to address these requirements.
• Participate in the development and integration of components-such as web-based user interfaces, access control mechanisms, and commercial indexing products-for creating an operational information and knowledge discovery system.
• Perform other duties as assigned.

Additional job responsibilities, at the SES.2 level

• Contribute to multiple parallel tasks and priorities of customers and partners, ensuring deadlines are met.
• Solve abstract problems, converting them into useable algorithms and software modules.
• Provide solutions that require analysis of multiple factors and the creative use of established methods.

• 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 technical field, or the equivalent combination of education and related experience.
• Fundamental knowledge of one or more of the following: scientific data analysis, statistical analysis, knowledge discovery, supervised learning, unsupervised learning, deep learning, reinforcement learning, natural language processing, and big data technologies.
• Knowledge of adversarial AI methods, including evasion attacks, privacy attacks, and data poisoning attacks.
• Skilled in all aspects of the data science life cycle: feasibility / background research, data exploration, feature engineering, modeling, visualization, deployment
• Fundamental experience developing data science algorithms with C++, Python, or R in Linux, UNIX, Windows environments, sufficient to integrate solutions into larger applications.
• Experience developing extensible and maintainable software leveraging software design principles.
• Experience with scikit-learn, PyTorch, TensorFlow, or similar machine learning (AI/ML) development API for the purpose of developing data science solutions.
• Ability to effectively handle concurrent technical tasks with conflicting priorities, to approach difficult problems with enthusiasm and creativity and to change focus when necessary, and to work independently and implement research concepts in a multi-disciplinary team environment, where commitments and deadlines are important to project success.
• 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

• Comprehensive analytical, problem-solving, and decision-making skills to develop creative solutions to complex problems.
• Broad experience with one or more of the following technical languages, concepts, or constructs: Python, scientific data analysis, statistical analysis, knowledge discovery, supervised learning, unsupervised learning, deep learning, reinforcement learning, natural language processing, and big data technologies.
• Proficient experience with at least one of the following advanced ML concepts: Transfer Learning, distributed ML (data/model), ML operations, generative models, Bayesian optimization, computer vision modeling, transformers, graph neural networks, uncertainty quantification, surrogate modeling, or techniques for data-poor ML (low-shot, coresets, etc).

#LI-Hybrid

Position Information

This is a Flexible Term appointment, which is for a definite period not to exceed six years.If final candidate is a Career Indefinite employee, Career Indefinite status may be maintained (should funding allow).

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

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