Refine Search Clear All
Engineering | livermore, CA | 05/08/2021
Job Code: SES.1 Science & Engineering MTS 1 / SES.2 Science & Engineering MTS 2
Position Type: Career Indefinite
Security Clearance: Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation)
Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
Medical Exam: Not applicable
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.
The Applied Statistics Group is seeking motivated and talented statisticians to assist in conducting basic and applied research in uncertainty quantification, statistical modeling, large scale parameter estimation, and advanced data analysis. At Lawrence Livermore National Laboratory (LLNL), we are developing game changing technologies enabled by our world class supercomputing facilities to represent and analyze the largest datasets in support of our national security and national science applications. These positions are in the Computational Engineering Division (CED) within the Engineering Directorate.
Additional job responsibilities (outlined below) will be assigned if hired at the higher level.
In this role you will
- Conduct data processing activities, including but not limited to understanding the data through the use of visualization and statistical analysis, data curation, and both fitting and evaluating state-of-the-art statistical models.
- Conduct surveys of state-of-the-art statistical modeling, uncertainty classification, Bayesian inference, design of experiments, statistical machine learning models, and algorithms relevant to the problem being addressed.
- Contribute to research efforts in statistics, uncertainty quantification, machine learning, and data analytics that enable development of new state-of-the-art solutions for Laboratory problem domains.
- Conduct implementation, training and validation of proposed new state-of-the-art models and algorithms for Laboratory problem domains.
- Contribute to the integration of algorithms within larger programmatic systems that require these capabilities.
- Participate in interactions with inter-organizational contacts and/or external customers.
- Assist in representing the organization by providing input on technical issues for specific projects including preparing and presenting technical reports.
- Perform other duties as assigned.
Additional job responsibilities at the SES.2 Level
- Research, develop, and apply solutions to moderately complex statistical problems of programmatic interest.
- Publish papers in peer reviewed journals and present results at scientific meetings and conferences.
- Contribute to proposals.
- Bachelor’s degree in Statistics, Applied Mathematics, Computer Science, Computational Engineering, Electrical Engineering, or the equivalent combination of education and related experience.
- Fundamental knowledge of and/or experience developing and applying algorithms in one or more of the following research areas: Bayesian inference, uncertainty quantification, unsupervised feature learning, active learning, reinforcement learning, ensemble methods, scalable online estimation, and probabilistic graphical models.
- Experience in the broad application of one or more higher level programming languages such as Python, Java/Scala, Matlab, R or C/C++.
- Experience with one or more machine learning libraries such as scikit-learn, MLlib,TensorFlow, PyTorch, Keras, Caffe or Theano.
- Experience working independently under general direction within the scope of an assignment and use sound judgment in determining methods, techniques, and evaluation criteria.
- Sufficient verbal and written communication skills necessary to effectively collaborate in a team environment and present technical ideas/results.
Additional qualifications at the SES.2 Level
- Comprehensive knowledge and experience with statistical modeling, density estimation, inference, and uncertainty quantification.
- Experience developing well-written, documented, and version-controlled code.
- Proficient verbal and written communication skills to collaborate in a multi-disciplinary team environment, publish and present technical ideas and inform management.
Qualifications We Desire
- Master’s degree in Statistics, Applied Mathematics, Computer Science, Computational Engineering, Electrical Engineering, or the equivalent combination of education and related experience.
Why Lawrence Livermore National Laboratory?
- Included in 2021 Best Places to Work by Glassdoor!
- Work for a premier innovative national Laboratory
- Comprehensive Benefits Package
- Flexible schedules (*depending on project needs)
- Collaborative, creative, inclusive, and fun team environment
Learn more about our company, selection process, position types and security clearances by visiting our Career site.
LLNL is a Department of Energy (DOE) and National Nuclear Security Administration (NNSA) Laboratory. Most positions will require a DOE L or Q clearance (please reference Security Clearance requirement). 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. In addition, all L or Q cleared employees are subject to random drug testing. An L or Q clearance requires U.S. citizenship. If you hold multiple citizenships (U.S. and another country), you may be required to renounce your non-U.S. citizenship before a DOE L or Q clearance will be processed/granted. For additional information please see DOE Order 472.2.
Equal Employment Opportunity
LLNL is an affirmative action and equal opportunity employer that values and hires a diverse workforce. 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.
If you need assistance and/or a reasonable accommodation during the application or the recruiting process, please submit a request via our online form.
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.