Computational Automation and Data Science 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

The Extreme Light, Matter, and Fusion Directorate (LMF), formerly the NIF & Photon Science Directorate, is seeking a Computational Automation and Data Science Scientist to leverage AI to support the automation, optimization, and integration of manual computational procedures used in scientific research, engineering, and laboratory operations. The successful candidate will develop data-analysis algorithms, machine-learning applications, database interfaces, and automated workflows that improve efficiency, repeatability, and decision-making.

This position will work within an interdisciplinary research and development environment and collaborate with scientists, engineers, technicians, software developers, and industrial partners. The selected candidate will translate scientific and engineering requirements into computational tools, automate existing analysis processes, manage scientific data, and support assigned software, data, and computational capabilities.

In this role, you will

• Review existing manual computational procedures and develop automated, scalable, and maintainable workflows under general direction. Work includes algorithms for data processing, feature extraction, classification, prediction, anomaly detection, and statistical analysis.
• Apply established machine-learning methods to scientific, engineering, laboratory, or operational data, including designing, training, validating, and evaluating models using appropriate performance metrics.
• Develop tools and scripts for data ingestion, cleaning, transformation, visualization, and analysis.
• Interface with databases and data systems, including developing queries, data pipelines, schemas, and data-management solutions.
• Integrate computational tools with laboratory instruments, engineering systems, software applications, and data repositories to create user-focused tools that improve the efficiency, consistency, and traceability of technical procedures.
• Document, test, validate, and maintain automated workflows, analytical tools, and operating procedures, and prepare technical reports, experimental plans, and procedures.
• Perform other duties as assigned.

Additional job responsibilities, at the SES.2 level

• Assess data quality, uncertainty, bias, reproducibility, and limitations of computational results. Develop methods for identifying trends, correlations, outliers, anomalies, and failure conditions in complex datasets using creative and innovative approaches; and recommend improvements to methods and processes.
• Contribute and maintain procedures for assigned computational, software, and data-analysis capabilities, including support for machine-vision or image-analysis methods, exploring a variety of approaches within established methods and contributing to the advancement of existing techniques.
• Prepare patents and peer-reviewed publications; and in open and classified environments, both internally and externally; provide informal guidance and mentoring to less-experienced personnel. Work independently with minimal direction while contributing effectively to multidisciplinary research and development teams.

Qualifications

• Ability to obtain and maintain a U.S. DOE Q-level security clearance, which requires U.S. citizenship.
• Bachelor of Science degree in computer science, data science, computational science, mathematics, statistics, engineering, or a related field, or an equivalent combination of education and relevant experience.
• Experience developing computational tools, scripts, algorithms, or automated workflows, including experience with data analysis, data processing, statistical methods, or scientific computing.
• Experience using machine-learning techniques for classification, regression, clustering, anomaly detection, prediction, or related applications.
• Experience with one or more programming languages commonly used for data analysis and automation, such as Python, MATLAB, R, C++, or a comparable language.
• Familiarity with databases, data structures, data modeling, SQL, application programming interfaces, or data-management systems.
• Sufficient verbal and written communication skills to collaborate effectively in a team environment, present and explain technical information, and learn and apply new computational methods, software tools, and technical concepts.

Additional qualifications, at the SES.2 level

• Comprehensive knowledge of computational and data-science concepts, demonstrating full competency.
• Relevant previous experience and demonstrated ability to perform independent work, including automating manual scientific, engineering, laboratory, or business processes.
• Broad application of technical principles, theories, concepts, and techniques in an applicable scientific or engineering discipline, in addition to working knowledge of other related fields, such as software-development practices (version control, testing, code review, documentation, and continuous integration).
• Experience establishing and maintaining procedures for computational, software, or data-analysis capabilities, and recommending process improvements as appropriate.
• Proficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information, including preparing technical reports, publications, presentations, or software documentation.
• Demonstrated ability to independently learn and apply new computational methods, software tools, and technical concepts, and to establish effective working relationships with multidisciplinary teams.

Qualifications We Desire

• Master of Science degree in computer science, data science, computational science, mathematics, statistics, engineering, or a related field, or an equivalent combination of education and relevant experience.
• Demonstrated experience automating manual scientific, engineering, laboratory, or business processes.
• Experience designing, training, validating, deploying, and maintaining machine-learning models, including deep learning, neural networks, time-series analysis, computer vision, image processing, or natural-language processing.
• Experience developing machine-vision systems or image-analysis algorithms.
• Experience integrating software with laboratory instruments, sensors, robotic systems, or engineering tools.
• Experience developing standard operating procedures, laboratory work instructions, computational workflows, software documentation, or data-management plans.
• Experience with cloud computing, high-performance computing, containerization, or distributed data processing.
• Experience with uncertainty quantification, experimental design, statistical modeling, or physics-informed machine learning, and ability to assess model performance, interpretability, robustness, generalizability, and operational limitations.
• Experience with database design, SQL optimization, data warehousing, data lakes, or scientific data repositories; developing data pipelines, application programming interfaces, dashboards, or user-facing analytical tools; and a record of publications, patents, presentations, software products, or other technical contributions in computational science, data science, machine learning, automation, or a related field.

Pay Range

$121,830 - $185,544 Annually

$121,830 - $154,500 Annually for the SES.1 level

$146,340 - $185,544 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.

Additional Information

#LI-Onsite

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

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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. 

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job 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.