Data Scientist - Uncertainty Quantification

Lawrence Livermore National Laboratory
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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.


  • 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.

Company Info.

Lawrence Livermore National Laboratory

Lawrence Livermore National Laboratory (LLNL) is a research institution, It's primary mission is to strengthen the United States' security by developing and applying advanced science, engineering, and technology in the areas of national security, nuclear deterrence, and stockpile stewardship. The laboratory is also involved in fundamental research and development in fields such as energy, biomedicine, materials science, and environmental science.

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    Livermore, CA, USA
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