Computational Biology / Statistical Genetics / Genomics


About The Position

Experience analyzing high-dimensional data (gene expression/RNA-seq, SNPs/SNVs, WES, WGS, and proteomic data), using computational methods to facilitate the development of new medicines and understand clinical cohorts. Experience with variant calling and interpretation. Ability to apply learning methods and regression analyses of genetic / genomic data to continuous and categorical responses or phenotypes.


Job Responsibilities

  • Analyze and interpret data such as gene expression, SNPs, NGS (RNA-Seq, WES/WGS), proteomic data, and clinical metadata from both a biological and clinical perspective
  • Collaborate with colleagues to develop innovative analysis methods and algorithms that solve complex computational research problems
  • Present scientific material (written and oral) to diverse audiences

Minimum Qualifications

  • PhD in Computational Biology, Biostatistics or a related field (i.e., Biology, Engineering, Computer Science, Mathematics, Bioinformatics, Statistics) or 5+ years of work experience at a leading computational biology focused institution
  • An equivalent combination of education and experience may be accepted as a satisfactory substitute for the specific education and experience listed above
  • Proven ability to work independently as well as contribute to larger initiatives
  • Effective English communication skills (both written and oral)
  • Proficient in at least one of R, Python, MATLAB, or a similar language


Preferred Additional Qualifications

  • Postdoctoral experience in a top lab using computational biology and/or biostatistics
  • Broad and deep understanding of genetics, proteomics, and/or genomics as documented by a strong publication record in high-impact journals
  • Strong experience in scientific communication; ¬†excellent writing and presentation skills
  • Demonstrated experience developing novel algorithms to address complex scientific problems
  • Proficient in R
  • Hands-on experience with samtools, IGV, GATK, bwa or similar, RSEM or similar
  • Experience applying machine learning approaches to analysis of heterogeneous clinical and pre-clinical data

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