Front End Data Science Developer Jobs (NOW HIRING)

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.

We have multiple openings for a Data Science Engineer with a background in applied machine learning and data science for cybersecurity and power systems applications. You will design, build, and deploy novel data science capabilities to enhance the reliability and adversarial resilience of critical infrastructure. You will write code, create analytical tools and visualizations, diagnose complex systems, and discover innovative approaches to challenging problems. These positions are in the Computational Engineering Division (CED), within the Engineering Directorate, in support of Global Security's Energy and Homeland Security (E) program.

Depending on your assignment, these positions may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.

These positions 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

• Design, develop, and apply machine learning and data science algorithms, including deep learning and modern AI techniques such as neural networks, transformers, and generative models, to analyze cybersecurity and power systems data.
• Analyze data and build analytical capabilities to improve the reliability and adversarial resilience of critical infrastructure.
• Write code to implement and deploy data science solutions and analytical tools, create visualizations, and follow software engineering best practices for code quality, testing, and documentation.
• Collaborate with multidisciplinary teams including cybersecurity experts, power systems engineers, and computer scientists.
• Support building research prototypes and capabilities for critical infrastructure protection, contributing to the development of new methodologies and tools.
• Provide solutions to moderately complex to complex data analytics challenges in the cybersecurity and power systems domains, using established and innovative methods.
• Perform other duties as assigned.

Additional job responsibilities, at the SES.3 level

• Lead highly complex projects with technical and analytic challenges, developing innovative solutions and building advanced capabilities.
• Discover and pioneer new approaches to data science problems, pushing the boundaries of current methodologies, and transforming ideas from concepts to operational solutions.
• Present technical work and results to sponsors and technical audiences on a regular basis, demonstrating capabilities through hands-on demonstrations and deep technical discussions.
• Contribute to technical direction and strategy for data science capabilities in critical infrastructure protection by building proof-of-concept systems, demonstrating new approaches, and contributing ideas to research proposals.

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
• Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related technical field, or the equivalent combination of education and related experience.
• Broad experience with Python programming and software development.
• Comprehensive experience applying machine learning, deep learning, or data science methods to real-world problems.
• Intermediate knowledge of software engineering best practices including version control, unit testing, and documentation.
• Proficient verbal and written communication skills necessary to collaborate within a team environment and present technical information to varied audiences.
• Effective interpersonal skills and initiative necessary to interact with all levels of personnel and work independently in a collaborative, multidisciplinary team environment.
• Demonstrated ability to balance multiple projects and prioritize competing demands while maintaining high-quality standards for deliverables.

Additional qualifications at the SES.3 level 

• Advanced experience in applied machine learning and data science with demonstrated ability to deliver complex technical solutions independently.
• Advanced experience building innovative data science systems and discovering novel approaches to complex problems.
• Experience presenting technical work and demonstrations to both technical and non-technical audiences, including sponsors and stakeholders.

Qualifications We Desire

• Master's degree or PhD in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related technical field.
• Experience with modern machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, Keras, and/or similar tools.
• Experience with deep learning techniques, transformer models, retrieval-augmented generation (RAG), fine-tuning pre-trained models, or adapting foundation models for specific application domains.
• Knowledge of cybersecurity principles and practices, including threat detection, anomaly detection, or security analytics.
• Experience with power systems, SCADA systems, industrial control systems, or operational technology environments.
• Experience with data visualization and effectively communicating analytical results to diverse audiences.

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.

#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. 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. 

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.