Position Summary:

Gritstone Oncology is seeking a Bioinformatics Scientist (Proteomics) to help harness the immune system to treat cancer. The Bioinformatics Scientist will perform analyses of industry leading proprietary and public proteomic and genomic datasets to discover novel targets for cancer immunotherapy and develop other actionable biological insights. 

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:

  • Design and implementation of computational methods for high-throughput analysis of mass spectrometry (e.g., HLA peptide sequencing) data. Refine methodologies for data storage, processing and visualization, and optimize performance and usability.
  • Analyze both small and large-scale biological data, integrating proteomics and genomics
  • In collaboration with the broader team, develop and improve methods to extract maximal biological insight from both internal and external data sources.
  • Effectively communicate findings to stakeholders

 Requirements:

Minimum Education/Experience:

  • PhD in Bioinformatics, Computer Science, Biostatistics, or a related field.  Or MS plus 3+ years of comparable work experience.
  • Significant experience working with proteomics data in a scientific research environment.
  • Expertise in numerical data analysis and scientific programming.
  • Self-motivated and able to work independently or in a small team with minimal supervision. Excellent communication and organizational skills.

 Qualifications:

Desired Experience:

  • In depth understanding of mass spectrometry laboratory techniques
  • Applying in-depth knowledge of cancer biology
  • Fluency in python including numpy/scipy/pandas
  • Software engineering including writing production-level code
  • Working in the cloud or on a high-performance computing cluster