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Metabolomics Associate Data Scientist – 95374
Organization: EB-Environ Genomics & Systems Bio

Berkeley Lab’s Environmental Genomics & Systems Biology (EGSB) Division is looking for an Associate Data Scientist to join their Metabolomics Research Group!

In this exciting role, you will have the opportunity to develop and apply algorithms, user interfaces, and new programmatic capabilities to accelerate compound identification and metabolomics data analysis. This position will develop Python methods, processes, and systems to consolidate and analyze unstructured, diverse “big data” sources to generate actionable insights and solutions for addressing research questions using metabolomics and genomics datasets. You will develop and code software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources and identify meaningful insights from large data and metadata sources; interpret and communicate insights and findings from analysis and experiments to product, service, and business managers.

The development work and analysis algorithms will be performed on high-performance computing hardware at the National Energy Research Scientific Computing Center (NERSC). As part of the team, you will identify weaknesses in the current code-base and workflows, propose solutions, and implement them using best practices. In addition to developing metabolomics workflows, custom analysis, and new algorithms for metabolomics at the Lab, you will be expected to contribute to papers that showcase integration of biological sequence data with metabolomics data. You will work alongside other scientific staff, postdocs, and students supporting data analysis of experiments, for example modeling microbial food webs, microbial community design, optimizing production of soil metabolites that build Carbon, etc.

What You Will Do:

  • Develop and apply innovative computational approaches to accelerate the analysis of mass spectrometry data.
  • Implement existing and new approaches for spectral networking, accelerate searches to aid in compound identification, and integration of metabolic pathways.
  • Collaborate with other members of the metabolomics research team to analyze datasets and write manuscripts.
  • Develop and implement algorithms to perform complex calculations efficiently on mass spectrometry data (e.g., spectral similarity scoring).

What is Required:

  • A Bachelor’s Degree Computer Science, Data Science, Engineering, Biology, Microbiology, Biochemistry, or a related field and a minimum of 2 years of related experience or an equivalent combination of education and experience.
  • Experience with Python programming, creating web applications, the linux shell, version control, unit-testing and test-driven design.
  • Excellent oral and written communication skills including experience documenting and presenting research results.
  • Demonstrated interpersonal skills including the ability to work within a diverse and collaborative interdisciplinary team environment.

What We Prefer:

  • Experience working with metabolomics, mass spectrometry, microbiology or environmental datasets.
  • Ability to understand metabolomics, mass spectrometry data and physical chemistry as demonstrated through generation of useful software for analyzing mass spectrometry data.
  • Ability to integrate findings from mass spectrometry data with other types of data using bioinformatic tools and mine these integrated datasets for relevant findings.
  • Lab experience with basic microbiology, molecular biology and chemistry.
  • Experience with other programming languages, statistics, visualization, and web development.
  • Knowledge of mass spectrometry techniques and data analysis methodologies.
  • Knowledge of chemistry and biology concepts relevant for effective data analysis.
  • Familiarity with cutting edge data analysis approaches in machine learning.

For full consideration, please apply by April 22, 2022 with the following application materials:

  • Cover Letter – Describe your interest in this position and the relevance of your background.
  • Curriculum Vitae (CV) or Resume.


  • This is a full time, exempt from overtime pay (monthly paid), 1 year (benefits eligible) Term appointment with the possibility of renewal up to a maximum of 5 years total or conversion to Career appointment based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs.
  • Salary is commensurate with experience.
  • This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
  • This position is eligible for a hybrid work schedule – a combination of teleworking and performing work on site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. Work schedules are dependent on business needs. Individuals working a hybrid schedule must reside within 150 miles of Berkeley Lab.

How To Apply
Apply directly online and follow the on-line instructions to complete the application process.

Learn About Us:
Berkeley Lab (LBNL) addresses the world’s most urgent scientific challenges by advancing sustainable energy, protecting human health, creating new materials, and revealing the origin and fate of the universe. Founded in 1931, Berkeley Lab’s scientific expertise has been recognized with 13 Nobel prizes. The University of California manages Berkeley Lab for the U.S. Department of Energy’s Office of Science.

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Berkeley Lab is an Equal Opportunity and Affirmative Action Employer. We heartily welcome applications from women, minorities, veterans, and all who would contribute to the Lab’s mission of leading scientific discovery, inclusion, and professionalism. In support of our diverse global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.

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