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ChemBio Data Scientist
Information Technology/Computing | livermore, CA | 08/24/2022
Job Code: SES.2 Science & Engineering MTS 2 / SES.3 Science & Engineering MTS 3
Position Type: Career Indefinite
Security Clearance: None (however, assignments longer than 179 days require 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.
We have openings for Chemical/Biological Data Scientists. You will perform work in a challenging R&D environment in support of the Laboratory's programs that integrate molecular dynamics simulations, machine learning, structural bioinformatics, modeling, computer science, and large experimental datasets in the areas of antibody and vaccine design. This position is within the Global Security Computing Applications Division GS-CAD of the Computing Directorate.
This position will be filled at either the SES.2 or SES.3 level depending on your qualifications. Additional job responsibilities (outlined below) will be assigned if you are selected at the higher level.
In this role you will
- Collaborate with scientists and researchers in one or more of the following areas: machine learning, statistical learning, antibody engineering, protein structure modeling and analysis, protein-protein interface analysis, information visualization, data integration, scientific data mining, database technology, scalable tool development, and High Performance Computing (HPC) simulation and evaluation.
- Develop machine learning models for predicting properties of antibodies and antigens using large experimental and synthetic datasets, while working with other LLNL scientists and application developers.
- Carry out development of moderately complex data analysis algorithms to address program and sponsor data sciences requirements.
- Engage other developers frequently to share relevant knowledge, opinions, and recommendations, working to fulfill deliverables as a team.
- Design technical solutions with limited direction, participate as a member of a multidisciplinary team to design, and implement software and perform analyses to address project requirements.
- Perform other duties as assigned.
Additional job responsibilities, at the SES.3 level
- Lead design and development of a data science project to ensure complex tasks and deadlines are met.
- Complete complex projects/tasks, and solve abstract complex problems/ideas and convert them into useable algorithms/software modules, while collaborating with team members.
- Provide solutions that require in-depth analysis of multiple factors and the creative use of established methods.
- Bachelor’s degree in Computer Science, Computer Engineering, Computational Biology, Computational Chemistry, or related field, or the equivalent combination of education and related experience.
- Comprehensive knowledge of one or more of the following: scientific data analysis, statistical analysis, deep learning, unsupervised learning, active learning, data management technologies, protein structure analysis, antibody engineering.
- Experience with molecular simulations or statistical modeling of experimental data in molecular biology.
- Broad experience developing software with Python or R in Linux or Windows.
- Broad experience with data analysis algorithms, data management approaches, relational databases, or machine learning algorithms.
- Experience with one or more deep learning libraries, such as, TensorFlow, Torch, Caffe, Keras, or Theano.
- Effective interpersonal skills necessary to interact with all levels of personnel.
- Proficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information.
Additional qualifications, at the SES.3 level
- Significant experience developing one or more of the following: machine learning models trained on molecular simulation data, active learning to guide model development, or uncertainty quantification on deep learning models.
- Significant experience developing and/or training deep learning models on molecular biology data.
- Advanced analytical, problem-solving, and decision-making skills to develop creative solutions to complex problems, as well as advanced verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information and provide advice to management.
Qualifications We Desire
- Master’s degree or Ph.D. in Computer Science, Computer Engineering, Computational Biology, Computational Chemistry, or related field.
- Publication record demonstrating research in the area of machine learning and/or computational biology or computational chemistry.
- Experience in protein structure bioinformatics, protein structure modeling, homology modeling, protein structure alignment, protein-protein interface analysis, protein-protein docking, binding prediction, computational protein chemistry, and/or computational immunology.
- Experience with machine learning and/or bioinformatics on large data sets of proteins.
- Experience working with data from phage and/or yeast display libraries.
Additional InformationAll your information will be kept confidential according to EEO guidelines.
This is a Career Indefinite position. Lab employees and external candidates may be considered for this position.
Why Lawrence Livermore National Laboratory?
- Included in 2022 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.
None required. However, if 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.) For additional information, please see DOE Order 472.2.
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.
Equal Employment Opportunity
LLNL is an equal opportunity employer that is committed to providing candidates and employees with a work environment free of discrimination and harassment. We value and hire a diverse workforce as it is a vital component of our culture and success. 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.
At LLNL, 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 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.