Applied Statistics Researcher - Security Clearance Required

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 dedicated to fostering a culture that values individuals, talents, partnerships, ideas, experiences, and different perspectives, recognizing their importance to the continued success of the Laboratory's mission.

We have an opening for an Applied Statistics Researcher
. You will engage in cutting edge research, design, and deployment of statistical methods to solve important decision and detection problems stemming from the Laboratory's core mission spaces. We invite you to join us if you have expertise in one of the following desired areas:
Bayesian modeling, uncertainty quantification, analysis and design of computer experiments, statistical learning, statistical methods for Big Data, or general statistical consulting. This position is in the Computational Engineering Division (CED), within the Engineering Directorate.

This position will be filled at either level based on knowledge and related experience as assessed by the hiring team. Additional responsibilities (outlined below) will be assigned if hired at the higher level.

You will

• Contribute to and actively participate in research, development, and execution within one or more of the following areas: information retrieval and representation, cyber security, image and video analysis, design of computer experiments, climate modeling, energy analysis, computational biology, lasers, and optics.
• Design, implement, and analyze techniques in one or more of the above areas.
• Contribute to and actively participate with project scientists and engineers in the scope, planning, and formulating modeling/simulation efforts for physical, engineering, and computational systems in the areas of cyber security, biological and environmental threat detection, uncertainty quantification, and others.
• Develop, implement, validate, and document specialized analysis software tools and models as required.
• Collaborate and communicate with others in a multidisciplinary team environment, including industrial and academic partners, project managers, and external sponsors, to deliver results and accomplish research goals.
• Organize, analyze and publish research results in peer-reviewed scientific or technical journals and present results at external conferences seminars and/or technical meetings.
• Perform other duties as assigned.

• Provide technical leadership and guidance to project teams developing state of the art methods and applying research results to meet programmatic goals, while balancing priorities of customers and partners to ensure deadlines are met.
• Serve as the primary technical point of contact for program managers internally and at sponsor and partner organizations.
• Utilize advanced knowledge to provide recommendations on methodologies and to influence deliverables to best meet sponsor needs.
• Mentor and advise LLNL scientists and engineers in applied statistics best practices.

• This position requires an active Department of Energy (DOE) Q-level clearance or active Top Secret clearance issued by another U.S. government agency at the time of hire.
• Master's degree in Statistics or other related technical field, or the equivalent combination of education and related experience.
• Comprehensive knowledge and experience using programming skills in at least one prototyping language R/Matlab/Python, as well as one of C/C++/Java to enable high performance statistical computation.
• Experience developing and applying advanced statistical/machine learning models and algorithms for one or more of the following settings: classification, clustering, anomaly detection, density estimation, pattern recognition, knowledge discovery.
• Experience developing independent research projects, either through previous work experience or as demonstrated through publication of peer-reviewed literature.
• Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
• Demonstrated initiative, effective interpersonal skills, and ability to work in a collaborative, multidisciplinary team environment.
• Ability and desire to obtain substantial domain knowledge in fields of application and ability to communicate effectively with subject matter experts.

• Advanced knowledge and significant experience in developing and applying advanced statistical/machine learning models and algorithms for one or more of the following settings: classification, clustering, anomaly detection, density estimation, pattern recognition, knowledge discovery.
• Significant experience executing independent research…