Join us and make YOUR mark on the World!
Come join Lawrence Livermore National Laboratory (LLNL) where we apply science and technology to make the world a safer place; now one of 2020 Best Places to Work by Glassdoor!
The Applied Statistics Group (ASG) is seeking talented researchers with expertise in uncertainty quantification, statistical modeling, machine learning, large-scale parameter estimation, and advanced data analysis. ASG staff members support a diverse set of Laboratory mission areas, such as material science, stockpile stewardship, space science, energy security and distribution, climate, nonproliferation, biodefense, cyber security, predictive medicine, advanced computing, simulation, and National Security applications. These positions are in the Computational Engineering Division (CED), within the Engineering Directorate.
This position will be filled at either the SES.3 or SES.4 level depending on your qualifications. Additional job responsibilities (outlined below) will be assigned if you are selected at the higher level.
-Scope, plan, and formulate advanced statistical modeling efforts for physical, engineering, and computational systems relevant to programmatic requirements.
-Identify and define complex problems stemming from programmatic research, propose methodologies, collect and analyze data, and document results.
-Adapt and apply existing statistical methods and theories to new problem domains.
-Identify, acquire, and maintain expertise in new subjects when necessary.
-Develop and maintain collaborations with researchers and/or stakeholders.
-Perform other duties as assigned.
In Addition, at the SES.4 Level
-Act independently as a project subject matter expert and as the primary technical contact to external collaborators and stakeholders.
-Set broad scientific objectives and influence technical direction of projects.
-Provide technical leadership for highly complex projects and upskill team members.
-Balance resources to ensure both long-term and short-term program objectives are met.
-Master’s degree in Statistics, Applied Mathematics, Computer Science, Computational Engineering, Electrical Engineering, or the equivalent combination of education and related experience.
-Advanced experience in the application of one or more higher level programming languages such as Python, Java/Scala, Matlab, R, or C/C++.
-Significant experience developing and applying advanced statistical/machine learning models and algorithms for one or more of the following areas: classification, clustering, anomaly detection, density estimation, pattern recognition, knowledge discovery, regression, inference, or optimization.
-Advanced verbal and written communication skills necessary to prepare and present papers, reports, proposals, and document results for audiences at all levels.
-Ability and interest in acquiring substantial domain knowledge in fields of application and ability to communicate effectively with subject matter experts.
-Ability to work independently, as well as part of a multidisciplinary team.
In Addition, at the SES.4 Level
-Highly advanced experience in providing expert level technical leadership in data science related projects and providing solutions to highly complex problems.
-Subject matter expertise in applied statistical modeling, in one or more of the following areas: classification, clustering, anomaly detection, density estimation, pattern recognition, knowledge discovery, regression, inference, or optimization.
-Expert communication, facilitation, and collaboration skills necessary to maintain partnerships and advise management.
-Ph.D. degree in Statistics, Applied Mathematics, Computer Science, Computational Engineering, Electrical Engineering, or the equivalent combination of education and related experience.
-Experience with large-scale computational modeling and/or parallel computing.
-Publication record in peer-reviewed journals.
-Experience working with Department of Energy, Department of Defense, Department of Homeland Security, and/or other relevant government agencies.
Pre-Employment Drug Test: External applicant(s) selected for this position will be required to 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.
Security Clearance: This position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on the particular assignment.
If you are selected and a security clearance is required, 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. L and Q-level clearances require 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.
If no security clearance is required, but your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process.This process includes completing an online background investigation form and receiving approval of the background check. (This process does not apply to foreign nationals.)
Note: This listing has three openings; to be filled as Career Indefinite or as At Will appointments. Lab employees and external candidates may be considered for these positions.
Lawrence Livermore National Laboratory (LLNL), located in the San Francisco Bay Area (East Bay), is a premier applied science laboratory that is part of the National Nuclear Security Administration (NNSA) within the Department of Energy (DOE). LLNL's mission is strengthening national security by developing and applying cutting-edge science, technology, and engineering that respond with vision, quality, integrity, and technical excellence to scientific issues of national importance. The Laboratory has a current annual budget of about $2.3 billion, employing approximately 6,900 employees.
LLNL is an affirmative action/ equal opportunity employer. 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, protected veteran status, age, citizenship, or any other characteristic protected by law.