Bioinformatician II - Genetics & Genomic Sciences

The Bioinformatician II partners with researchers/biologists from the Mount Sinai Department of Genetics and Genomic Sciences on innovative genomic analyses of structural variation, epigenetics, and gene expression in an integrated environment combining experimental and bioinformatic approaches to human disease. S/he leads a variety of projects and gains exposure to many productive areas of research, and develops/supports multiple research projects by delivering bioinformatics services for bio-medical data from cutting-edge genomic technologies.

This incumbent performs the role of database developer for Varimpact, building a comprehensive impact database of genetic variants through text mining. This database consists of genetic variants with gene, protein, pathway, cellular, drug level functional impact and is a tool to search and highlight genetic variants, drugs and diseases in 30 million abstracts and 3 million full texts.

• Develops and may perform a lead role for a variety of bioinformatics analysis projects, including development of Varimpact, an impact database of generic variants.
• Writes custom scripts for analysis of genomic data derived from high-throughput techniques.
• Manages databases, conducts statistical and genomic analysis, and participates in the preparation of manuscripts and presentations.
• Collaborates with wet-bench biologists and works independently to analyze data derived from several types of projects using microarrays and high-throughput sequencing.
• Conducts analysis of microarray and high-throughput sequencing data, genome sequence analysis, experimental design and statistical analysis.
• Performs quality control and analysis.
• Other related duties as assigned.

• M.S. in Bioinformatics, Biomedical Informatics, Computational Biology, or Genomics. Alternately, M.S. in a discipline requiring strong computational and analytical skills supplemented with some biology exposure. Ph.D in a related field preferred. Those with a Bachelors degree and additional post-graduate experience are considered.
• 2+ years post-graduate experience in a research environment, including the manipulation of large biological datasets.
• Advanced knowledge of genetics and/or statistical analysis software and online resources. Experience in programming environments such as MatLab, R statistical package, BioConductor, Perl and C++.