Data Scientist - Uncertainty Quantification

Company Description

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 committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is essential to innovation and creativity for continued success of the Laboratory’s mission.

Pay Range

$123,960 - $166,992 Annually for the SES.2 level

$148,650 - $200,328 Annually for the SES.3 level

Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity.

Job Description

We have multiple openings for Data Scientists with expertise in Uncertainty Quantification to join a vibrant team supporting multiple missions across LLNL, including applications in biology, physics, and space programs. You’ll work with domain scientists and engineers to develop and apply machine learning, uncertainty quantification, statistical and data science methods to a variety of important and challenging national security problems. You will also have the opportunity to engage in a variety of related research projects in computational physics, high-performance computing, modeling and simulation (mod/sim), and data analysis. These positions are in the Center for Applied Scientific Computing (CASC) within the Computing Directorate.

These positions will be filled at either level 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

• Research and develop advanced estimation, verification, and validation (V&V), and uncertainty quantification (UQ) methodology using state-of-the-art statistical methods, machine learning, and/or multi-fidelity modeling.
• Adapt current machine learning research to high consequence real world applications and integrate solutions into practical tool chains.
• Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external to the Laboratory.
• Present formal and informal overviews of research progress to peers, sponsors, and stakeholders.
• Work as part of inter-disciplinary teams of scientists and engineers to achieve mission-oriented deliverables on externally driven timelines.
• Perform other duties as assigned.

Additional Responsibilities at the SES.3 level

• Manage multiple advanced parallel tasks and priorities of customers and stakeholders, ensuring deadlines are met, while leveraging team member’s skills.
• Conduct self-initiated research, maintain an awareness of technical literature in assigned areas, publish research results in peer-reviewed scientific or technical journals, and present results at external conferences, seminars, and/or technical meetings.
• Provide solutions to complex problems that require in-depth analysis of tangible and intangible factors.

Qualifications

• Ability to obtain and maintain a US DOE Q and SCI security clearance which requires U.S. Citizenship.
• Ph.D. in Computer Science, Statistics, Mathematics, or a related field, or equivalent level of education and experience.
• Experience in one or more of the following: machine learning, deep learning, artificial intelligence, scientific machine learning, image analysis, uncertainty quantification, verification and validation (V&V), computational statistics.
• Experience with scientific programming and algorithm development in Python/R/Julia, or C/C++.
• Comprehensive analytical and problem-solving skills necessary to independently craft creative solutions to solve complex problems.
• Ability to conduct high quality research and to develop implementations of sophisticated algorithms to evaluate the results.
• Advanced verbal and written communication skills to effectively document, present and explain technical information to technical as well as non-technical audiences.

Additional Qualifications at the SES.3 Level

• Experience leading or mentoring small 2-3 person teams.
• Significant experience developing and applying advanced UQ, V&V and/or ML methods to real-world problems.
• Significant experience working on a research team with individuals from diverse technical backgrounds, as well as an ability to function as an independent researcher with a high level of attention to details.

Qualifications We Desire

• Experience in computational physics modeling, modeling & simulation (mod/sim), digital design, surrogate modeling of computer experiments, or scientific high-performance computing.
• Experience in modern machine learning environments (e.g., TensorFlow/Keras, PyTorch and related data ecosystems).
• Experience with common Unix/Linux tools and version control.
• Experience with high performance computing (HPC) and batch environments.
• Experience in software development as shown by open-source projects, scientific collaboration contributions, or an employment background that includes software engineering roles or responsibilities.
• Expertise with scientific computing workflow systems and deployment of data processing pipelines on various computing architectures.
• Experience working in the U.S. national security community.

Additional Information

All your information will be kept confidential according to EEO guidelines.

Position Information

This is a Career Indefinite or At Will appointment. Lab employees and external candidates may be considered for this position.

Why Lawrence Livermore National Laboratory?

• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Inclusion, Diversity, Equity and Accountability (IDEA) - visit https://www.llnl.gov/diversity
• Our core beliefs - visit https://www.llnl.gov/diversity/our-values
• Employee engagement - visit https://www.llnl.gov/diversity/employee-engagement

Security Clearance

This position requires a Department of Energy (DOE) Q-level clearance. Also, you must have the ability to obtain and maintain Sensitive Compartmented Information (SCI) access. 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.

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.

We invite you to review the Equal Employment Opportunity posters which include EEO is the Law and Pay Transparency Nondiscrimination Provision.

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.