• South San Francisco, CA
  • Full Time
  • Computing


Who we are:

Calico is a research and development company whose mission is to understand the biology of aging, and to help people to lead longer and healthier lives. We aim to combine the best biomedical science with cutting edge technology and computing. We are a nimble, fast-paced startup, while also having secure, long-term funding.

Position description:

To understand aging, we need to study the process by which individuals change over time: the diversity of phenotypic trajectories that individuals undergo as they age, and the causal factors that might underlie the variation. Calico is seeking a biostatistician to work on the design and modeling of longitudinal, preclinical experiments involving mouse and human cohorts with extensive genetic, multi-omic, and phenotypic data. You will be responsible for helping to design experiments, ensuring that they are well-constructed and adequately powered. You will also develop and implement novel statistical and machine learning techniques for analyzing the results of such experiments, both from in-house cohorts and from cohorts obtained from the outside. Your work will involve characterizing the trajectories of aging in phenotypic space; the identification of biomarkers involved in the aging process; and the use of statistical genetics to uncover the genetic factors that help give rise to these different trajectories. You will be working on a team encompassing diverse, cross-functional skills, which includes engineers, data scientists, biomedical scientists, and computational biologists.

Position requirements:

  • 4+ years of experience in statistical data analysis, including at least 2 years of hands-on work experience with real data (either in academia or in industry)
  • Strong coding skills and substantial experience coding in at least one of R or Python
  • Strong analytical and quantitative skills, including experience with state-of-the-art methods in applied statistics
  • Significant experience with longitudinal data, time series analysis, survival analysis, or epidemiological modeling  
  • Experience in one or more of the following areas: statistical genetics (including the use of public annotation data such as dbSNP, ClinVar, the human phenotype ontology, or similar), causal modeling, and/or use of mass spectrometry data
  • Track record of effective collaboration in a cross-functional environment with people of diverse backgrounds
  • MS/PhD in Computer Science, Statistics, Biostatistics, Bioinformatics, or related technical field, or equivalent practical experience
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