Computational Biologist - Biomarkers/Physiology Models

Calico - South San Francisco, CA

Full-timeEstimated: $82,000 - $120,000 a year
Who we are:
Calico is a research and development company whose mission is to understand the biology of aging and devise interventions that enable longer and healthier lives. Executing this mission will require an unprecedented level of interdisciplinary effort and a long-term focus for which funding is already in place.

Position description:
Calico is seeking a computational biologist with experience in mass spectrometry data analysis to study an association between molecular biomarkers and age-related diseases in longitudinal cohorts. The successful candidate will have a track record in integrative analysis of large phenotypic datasets (metabolomics, proteomics, lipidomics) and a passion for use of statistical modeling techniques to identify novel biomarkers and interpret relationships between molecular phenotype changes and physiological declines. The ideal candidate will work closely with mass-spectrometry and physiology teams to further translational efforts and will help with development of joint omics/physiology mathematical models.

  • Develop data analysis and modeling techniques for mass-spectrometry omics datasets in longitudinal cohorts.
  • Work in close collaboration with translational group to implement and interpret physiological data in model organisms.
  • Present results at internal and external meetings.
  • Publish results in scientific journals and release open source code.
  • 5+ years of experience in computational biology, including a PhD degree in biology, computer science, statistics or related fields; post-graduate experience (post-doc or industry) is a plus.
  • Experience working with mass spectrometry datasets including metabolomics, proteomics, and lipidomics.
  • Strong coding skills in structural languages C++/Python and/or statistical languages R;
Ability to work with others and to communicate effectively in person and in writing.
  • Strong interest in working on difficult problems.