Position Summary:

Gritstone Oncology is seeking a Director, Bioinformatics to help harness the immune system to treat cancer. The Director will lead an expert team performing analyses of industry leading proprietary and public genomic and proteomic datasets to find actionable biological insights, including real-time neoantigen identification for patients.

 We are looking for an independent and flexible team player who will thrive in a fast-paced, dynamic, and highly collaborative environment. This is an opportunity to work for a well-funded early biotechnology company where bioinformatics analyses drive personalized cancer treatments.

 Specific responsibilities:

  • Lead a team of experienced, creative bioinformaticians developing and performing novel, rigorous analyses in the research and clinical settings
  • Work closely with NGS, proteomics, and immunology lab scientists to design and interpret experiments
  • Manage implementation and application of informatics-related GMP quality systems
  • Coordinate high-volume, heterogeneous, complex data analysis under tight deadlines and stringent quality requirements
  • Ensure timely delivery of data to external partners and collaborators
  • Effectively manage team dynamics and stakeholders in a high-pressure, fast-paced environment
  • Willingness and ability to pitch in on method development and data analysis

Minimum Education/Experience:

  • Minimum 8 years experience in biotech/pharma industry, with 5+ years leading bioinformatics/computational biology teams
  • Small company experience preferred
  • PhD in Bioinformatics or a related highly quantitative field
  • Training and experience in applying rigorous statistical methods for hypothesis testing to biological data
  • Track record of success in analyzing Mass Spectrometry or NGS data, as evidenced for instance by key contributions to high-quality publications
  • Software engineering in python, including production-level code

 Desired Experience:

  • Applying in-depth knowledge of cancer biology
  • Analysis of large, publicly-available datasets (e.g. GTEx, TCGA, ExAC
  • Analysis of single cell data
  • Working on AWS