Lawrence Livermore National Laboratory
Senior Protein Design Data Scientist
Lawrence Livermore National Laboratory, Livermore, California, United States, 94551
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Mid-Senior Level | Full-time
Engineering | livermore, CA | 11/22/2024
Reference #:
REF6728E Job Code:
SES.2 Science & Engineering MTS 2 / SES.3 Science & Engineering MTS 3 Organization:
Engineering Position Type:
Career Indefinite Security Clearance:
None/Position does not require US citizenship (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 important for continued success of the Laboratory’s mission. Pay Range $93,204 - $204,636 Annually $93,204 - $170,556 Annually for the SES.2 level $111,804 - $204,636 Annually for the SES.3 level This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. Job Description
We have an opening for a
Team lead or Co-lead
for protein library data design, analysis, and dissemination. This role requires an interdisciplinary approach, including knowledge of data science, machine learning, and biological data. You will have a leading role in external and internal library efforts. You will provide leadership and fully-contextualized decision-making toward effective, efficient, and rapid library data generation. You will coordinate with internal and external stakeholders, including both short-term operational stakeholders and longer-term research stakeholders, particularly in machine learning; external and internal partners who perform the laboratory work to create and assay the properties of these libraries; and the data science, structural biology and other contributors who will be led in this broader effort. All of these components must be undertaken in the service of and in coordination with broader efforts. 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 responsibilities (outlined below) will be assigned if hired at the higher level. In this role you will: Contribute, participate, and work in coordination with partners and stakeholder teams to continue and expand:
the generation of library data; the analysis and design of library data; the dissemination of library data; the development of mature, effective workflows for library design in appropriate use cases.
Work under limited direction using independent judgment to provide solutions to problems of moderate complexity. Respond dynamically to unique, unexpected needs in library design by using creativity with established and/or creative methods. Perform other duties as assigned. Additional job responsibilities, at the SES.3 level: Provide overall technical leadership and guidance and serve as primary point of contact for library design. Provide solutions to complex problems using in-depth analysis and collaborate in the development of innovative methods/technology. Contribute to the definition and articulation of strategy for library data in the context of wider LLNL efforts to generate effective predictors for protein properties. Qualifications
PhD in Biology, Engineering, Computer Science, or related fields, or the equivalent combination of education and related experience. Comprehensive knowledge of, and previous experience with conversancy of proteins, protein structure, bioinformatics, experimental library generation, assays, sequencing, sorting, and other relevant biological domain knowledge. Comprehensive knowledge of, and previous experience with data science, statistics, and machine learning. Programming skills in Python including experience with collaborative development environments and practices. Knowledge of appropriate software for bioinformatics and structural biology. Proficient communication skills and demonstrated effectiveness in multidisciplinary settings, including a strong record of documentation of executed work. Ability to prioritize, balance, and keep several parallel threads of work in simultaneous, smooth motion. Additional qualifications at the SES.3 level: Significant experience leading interdisciplinary teams, including setting clear expectations, delegating to subordinates and peers, and ensuring successful, timely completion of objectives. Demonstrated ability and experience managing many parallel threads of work in simultaneous, smooth motion, in coordination with and leading a team of several employees. Ability to engage and negotiate with stakeholder input; dynamically reprioritize in response to resulting decisions. 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 Advanced level programming and data science skills, including demonstrated strong programming skills in Python. Knowledge of DNA synthesis techniques, library design, assembly, and deep sequencing. Advanced level knowledge of and previous experience with advanced protein machine learning techniques, such as AlphaFold, ESM, and RFDiffusion. Strong understanding of fundamental statistical and machine learning principles that underpin successful training of models with effective generalization capabilities, as well as experimental design. Strong understanding of the current state of the field of computational protein design, as well as the strategic implications of this understanding on LLNL efforts. Additional Information
#LI-Hybrid Position Information This is a Career Indefinite position, open to Lab employees and external candidates. Why Lawrence Livermore National Laboratory? Included in 2024 Best Places to Work by Glassdoor! Flexible schedules (*depending on project needs) 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.) Pre-Employment Drug Test External applicant(s) selected for this position must 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. Wireless and Medical Devices Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices. If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings. How to identify fake job advertisements Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under “Find Your Job” of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond. We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. 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. 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 use our online form to submit a request. 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. Operated by the Lawrence Livermore National Security, LLC for the Department of Energy's National Nuclear Security Administration. Learn about the Department of Energy's Vulnerability Disclosure Program.
#J-18808-Ljbffr
REF6728E Job Code:
SES.2 Science & Engineering MTS 2 / SES.3 Science & Engineering MTS 3 Organization:
Engineering Position Type:
Career Indefinite Security Clearance:
None/Position does not require US citizenship (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 important for continued success of the Laboratory’s mission. Pay Range $93,204 - $204,636 Annually $93,204 - $170,556 Annually for the SES.2 level $111,804 - $204,636 Annually for the SES.3 level This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. Job Description
We have an opening for a
Team lead or Co-lead
for protein library data design, analysis, and dissemination. This role requires an interdisciplinary approach, including knowledge of data science, machine learning, and biological data. You will have a leading role in external and internal library efforts. You will provide leadership and fully-contextualized decision-making toward effective, efficient, and rapid library data generation. You will coordinate with internal and external stakeholders, including both short-term operational stakeholders and longer-term research stakeholders, particularly in machine learning; external and internal partners who perform the laboratory work to create and assay the properties of these libraries; and the data science, structural biology and other contributors who will be led in this broader effort. All of these components must be undertaken in the service of and in coordination with broader efforts. 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 responsibilities (outlined below) will be assigned if hired at the higher level. In this role you will: Contribute, participate, and work in coordination with partners and stakeholder teams to continue and expand:
the generation of library data; the analysis and design of library data; the dissemination of library data; the development of mature, effective workflows for library design in appropriate use cases.
Work under limited direction using independent judgment to provide solutions to problems of moderate complexity. Respond dynamically to unique, unexpected needs in library design by using creativity with established and/or creative methods. Perform other duties as assigned. Additional job responsibilities, at the SES.3 level: Provide overall technical leadership and guidance and serve as primary point of contact for library design. Provide solutions to complex problems using in-depth analysis and collaborate in the development of innovative methods/technology. Contribute to the definition and articulation of strategy for library data in the context of wider LLNL efforts to generate effective predictors for protein properties. Qualifications
PhD in Biology, Engineering, Computer Science, or related fields, or the equivalent combination of education and related experience. Comprehensive knowledge of, and previous experience with conversancy of proteins, protein structure, bioinformatics, experimental library generation, assays, sequencing, sorting, and other relevant biological domain knowledge. Comprehensive knowledge of, and previous experience with data science, statistics, and machine learning. Programming skills in Python including experience with collaborative development environments and practices. Knowledge of appropriate software for bioinformatics and structural biology. Proficient communication skills and demonstrated effectiveness in multidisciplinary settings, including a strong record of documentation of executed work. Ability to prioritize, balance, and keep several parallel threads of work in simultaneous, smooth motion. Additional qualifications at the SES.3 level: Significant experience leading interdisciplinary teams, including setting clear expectations, delegating to subordinates and peers, and ensuring successful, timely completion of objectives. Demonstrated ability and experience managing many parallel threads of work in simultaneous, smooth motion, in coordination with and leading a team of several employees. Ability to engage and negotiate with stakeholder input; dynamically reprioritize in response to resulting decisions. 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 Advanced level programming and data science skills, including demonstrated strong programming skills in Python. Knowledge of DNA synthesis techniques, library design, assembly, and deep sequencing. Advanced level knowledge of and previous experience with advanced protein machine learning techniques, such as AlphaFold, ESM, and RFDiffusion. Strong understanding of fundamental statistical and machine learning principles that underpin successful training of models with effective generalization capabilities, as well as experimental design. Strong understanding of the current state of the field of computational protein design, as well as the strategic implications of this understanding on LLNL efforts. Additional Information
#LI-Hybrid Position Information This is a Career Indefinite position, open to Lab employees and external candidates. Why Lawrence Livermore National Laboratory? Included in 2024 Best Places to Work by Glassdoor! Flexible schedules (*depending on project needs) 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.) Pre-Employment Drug Test External applicant(s) selected for this position must 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. Wireless and Medical Devices Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices. If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings. How to identify fake job advertisements Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under “Find Your Job” of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond. We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. 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. 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 use our online form to submit a request. 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. Operated by the Lawrence Livermore National Security, LLC for the Department of Energy's National Nuclear Security Administration. Learn about the Department of Energy's Vulnerability Disclosure Program.
#J-18808-Ljbffr