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(Senior) Scientist, Computational Biology

Dark Horse Talent
Cambridge, Massachusetts, US
Closing date
May 17, 2022

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Academic / Research
Informatics / GIS
Salary Type
Employment Type
Full time
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Lead the use of computational genomics to support the discovery and development of novel gene-editing technologies.

You will develop and implement a strategy for novel protein discovery and contribute to advancing programs for proof-of-concept studies. You will contribute scientific, technical, and leadership expertise to a multidisciplinary team, emphasizing conceptualization, experimentation, data analysis, presentation, and strategic planning.

Opportunity to:
  • Utilize strategies from comparative genomics, evolutionary biology, and sequence analysis to propose and implement methodologies for novel protein discovery.
  • Lead the implementation of computational pipelines to mine large-scale genome sequencing data.
  • Work both independently and as part of a collaborative, multi-disciplinary team to design, analyze, and interpret multi-omics technologies focused on discovering novel gene writing systems.
  • Lead and mentor junior scientists to collectively achieve team goals.
  • Collaborate closely with experimental scientists to ensure that data is effectively utilized for high-level impact.
  • Present scientific findings to broad audiences including senior leadership to drive decision making in program teams

Basic Qualifications:
  • Ph.D. (or comparable experience) in Computational Biology, Bioinformatics, Genomics, Evolutionary Biology, or related discipline.
  • Proficient with experimental design, data processing, statistical analysis, and bioinformatics analysis/reporting of next-generation sequencing data.
  • Experience with strategies for genome-scale mining initiatives.
  • Experience in discovery research.
  • Fluency in one or more programming languages with bioinformatics applications (R, Python).

Nice to Have:
  • Experience in gene therapy and the development of gene editing platforms as therapeutics.
  • Strong competency in sequence analysis methods including gene identification, functional annotation, or comparative genomics.
  • Familiarity with short and long-read next-generation sequencing platforms (Illumina, PacBio, Nanopore).
  • Experience in virology or mobile genetic elements.
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