Bioinformatics Scientist

Bioinformatics Scientist
San Francisco, CA

Our client’s mission is to enable pharmaceutical and biotechnology companies to bring more cell and gene therapies to patients. Their rapidly growing and cross disciplinary team consists of creative bioengineers and synthetic biologists working alongside data scientists to create cutting-edge solutions to problems in gene therapy. 

Responsibilities

  • Lead the effort to aggregate results from our screening platform to extract key biological insights that drive the development of our engineered cell lines and IP portfolio.
  • Assist our cell engineering team in identifying specific genetic modifications required to meet the needs of each engineered cell product.   
  • Provide general computational biology and statistical domain expertise to our multidisciplinary project teams as needed. 
  • Develop pipelines and computational tools to integrate orthogonal data from assays including scRNA-seq, ATAC-seq, ChIP-seq, and similar methods to map genomic perturbations onto specific molecular functions.
  • Identify key public data sets and databases that can be used to enhance our understanding of the molecular underpinnings of important cell line phenotypes.
  • Work closely with our Software Development team to architect a LIMS and overall computational infrastructure to support the controlled, efficient processing of data from our core assays.

About You

  • PhD in bioinformatics, bioengineering, computer science, or a related computational field.
  • A publication record that demonstrates significant depth in the areas of mammalian gene regulation, functional genomics, and the assays that support these fields.   
  • A solid understanding of general molecular biology, next generation sequencing, long read sequencing, and associated techniques.
  • A strong understanding of basic statistics and its application to genomics and high throughput assays.
  • Experience developing scientific software in Python and R, including the use of relevant data science packages (e.g. SciPy, Bioconductor, etc.)
  • Experience developing parallelized software for deployment in a High Performance Compute (HPC) environment.
  • Strong interpersonal skills and the ability to communicate effectively with colleagues from different technical and scientific backgrounds in a multidisciplinary team.

Preferred

  • Experience with CRISPR library design and screen analysis.
  • Experience applying machine learning methods to biological datasets.
  • Experience supporting the deployment of production software and associated best practices