Senior Scientist, Systems Immunology & Data Sciences

  • Location:Cambridge, MA
  • Department:Surface Oncology
  • Type:Full Time
  • Min. Experience:Mid Level

We are looking for a motivated individual to drive our efforts in systems immunology and data sciences within the research organization at Surface Oncology. This individual will work closely with a team of scientists to generate, analyze and interpret high-dimensional biological data (scRNAseq, RNAseq, microarray) to support our drug development efforts.

 

RESPONSIBILITIES:

  • Analyze bulk and single-cell omics data (microarray, RNAseq, scRNA-seq, scATAC-seq & CITE-seq), flow cytometry data and proteomics data for biomarker discovery and mechanism of action studies
  • Interact in a collaborative environment to convert knowledge on potential biomarkers to testable hypothesis
  • Continuously evaluate new technologies and assays to advance biomarker and mechanism of action studies
  • Act as a scientific expert within the department that impacts the achievement of diverse project objectives
  • Prepares technical reports, summaries, protocols and quantitative analyses
  • Must work well in a group setting and have excellent written and verbal communication skills

EDUCATION AND EXPERIENCE: 

  • PhD (2+ years) or MS/BS (5+ years) in immunology, bioinformatics, computational biology, or closely related disciplines
  • Extensive knowledge in both bioinformatics and immunology
  • Experience analyzing high throughput omics data and discovering novel biomarkers; generating testable hypothesis
  • Fluency in R and Python for exploratory data science.
  • Hands on experience with Seurat/Scanpy/Kallisto and downstream scRNA processing tools
  • Experience building Shiny dashboards and visualizations with Tableau
  • Experience with Amazon Cloud, working with S3 storage and EC2 servers
  • Working knowledge of linux operation system, shell scripting, and version control systems such as Git
  • Experience in systems biology with integration of and statistical methods for mining omics data (transcriptomics genomics, epigenetics, proteomics)
  • Knowledge of immunology and experience with wet lab techniques such as cell culture, RNA isolation and FACS preferred