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Biological Data Science Fellow

Informatics | San Francisco, Ca | Temporary

Job Description

 About Us

The Parker Institute for Cancer Immunotherapy (PICI) is radically changing the way cancer research is done. Founded in 2016 through a $250 million gift from Silicon Valley entrepreneur and philanthropist Sean Parker, the San Francisco-based nonprofit is an unprecedented collaboration between the country’s leading immunotherapy researchers and cancer centers, including Memorial Sloan Kettering Cancer Center, Stanford Medicine, the University of California, Los Angeles, the University of California, San Francisco, the University of Pennsylvania and The University of Texas MD Anderson Cancer Center. The institute also supports top researchers at other institutions, including City of Hope, Dana-Farber Cancer Institute, Fred Hutchinson Cancer Research Center, Icahn School of Medicine at Mount Sinai, Institute for Systems Biology and Washington University School of Medicine in St. Louis.

By forging alliances with academic, industry and nonprofit partners, PICI makes big bets on bold research to fulfill its mission: to accelerate the development of breakthrough immune therapies to turn all cancers into curable diseases.

Help us create a world that doesn’t fear cancer. Join us.

 Overview of the Role

 The Biological Data Science Fellow will contribute to analyses of PICI Clinical Trial data with the goal of uncovering novel insights leading to mechanistic understanding, patient selection biomarkers, or new therapies. The Fellow will work collaboratively with other Informatics and Translational Medicine team members to analyze molecular data from a variety of assays, including but not limited to RNAseq, Whole Exome Sequencing, TCRseq, high-dimensional flow cytometry, cytokine measurements, and high-dimensional tissue imaging. Using PICI’s CANDEL data platform, the Fellow will perform primary processing of molecular data and then marry molecular and clinical data to uncover novel insights.

 This is a time-bound role with an initial time period of 1 year, with the opportunity for potential extension. The role is intended for someone new in their career seeking new experiences at the border of academia and industry with offering competitive compensation package including benefits. Substantial mentorship will be included as part of the role and can be remote during COVID-19 restrictions.  

 The ideal candidate will be a strong technical contributor, capable of working independently to analyze data and produce high-quality results. They will need to balance a mindset of scientific inquiry with a results-driven attitude suited to the quick pace of clinical trial delivery. Lastly, they should be a team player and quick learner, excited to learn to use new analysis tools as well as contribute to shared code.

 Reporting Structure and Team

The Biological Data Science Fellow reports to the Associate Director of Informatics and is a key member of the Informatics team.  This position is located in the San Francisco office post COVID and we offer remote opportunities until the office staff returns.  

 FLSA Status: Non-Exempt

 Essential Job Functions

  • Ingest and process molecular data from one or more assays (sequencing, flow/CyTOF, imaging, and more) using PICI’s established data processing and storage platform
  • Use R to explore data with a variety of methods, looking for associations between molecular features and clinical variables such as response, adverse events, or course of treatment
  • Contribute to a shared codebase of high-quality R code for repeatable molecular and clinical data analysis
  • Work closely with Translational Medicine group to determine questions of interest and refine results
  • Present results to stakeholders, including both internal and external meetings
  • Prepare content for conference presentations and publications

 Knowledge, Skills, and Experience

  • Advanced Degree in Bioinformatics, Statistics, Biology, or related discipline (Master’s degree minimum, PhD preferred)
  • 3+ years experience analyzing data in an academic or industry setting
  • Strong programming skills in R
  • Experience with ggplot2 or related visualization libraries
  • Experience with one or more molecular data types (sequencing, gene expression, flow/CyTOF, tissue imaging, or related)
  • Knowledge of statistical concepts relevant to translational research (hypothesis testing, survival analysis, regression, etc.)
  • Ability to communicate results to a variety of stakeholders, including technical and non-technical scientific audiences
  • Ability to work as a team player, respecting others and holding a “learner not knower” attitude
  • Ability to work independently in a multi-disciplinary environment
  • Bonus qualifications:
    1. Experience at the bench running flow/CyTOF, imaging, or sequencing assays
    2. Experience in Immuno-Oncology
    3. Experience with command line tools, cloud computing, and database technologies
    4. Experience with clinical research