Head of Computational Science

Boston, Massachusetts

  Computational Biology

Permanent

Our client is a Boston based Biotech, with venture capital backing, that is revolutionizing drug discovery and development as we know it. They are combining machine learning with omics technologies to improve people's lives.

We are looking for a Head of Computational Sciences to lead a team of data scientists, bioinformaticians, engineers, and wet lab scientists to find useful RNA sequence properties for future therapies. The chosen applicant must be able to operate in a dynamic, fast-paced multidisciplinary biotech environment. The ideal applicant will have good communication skills, be a strong team player, and have an inspiring leadership style.

Responsibilities:

  • Demonstrates leadership and proficiency in design, analysis, and reporting on both a strategic and tactical/technical level.
  • Delivering whole bioinformatics services, from project design to data delivery, is your responsibility.
  • Accountable for transferring prototype analysis pipelines into production, as well as maintaining and upgrading current production analysis pipelines.
  • Work cooperatively to plan, coordinate, and carry out the overall research and development plans with internal and external stakeholders.
  • Work independently and creatively to glean important insights from open data sources.
  • At important scientific conferences, present your research findings both internally and externally.
  • Assist in the creation of manuscripts and IP filings
  • Utilize network biology technologies in a novel way to help wet lab scientists find new targets.

Requirements:

  • Ph.D. in computational biology, bioinformatics or related field  
  • 12+ years of experience working in biotech/pharmaceutical industry
  • Experience in drug discovery, and specifically in single-cell drug creation / target identification
  • Extensive experience in RNA, bash/shell scripting proteomics, transcriptomics, and genomics analyses
  • Proficiency with standard computational biology tools and commonly used programming languages, such as Python, R, Perl, or C/C++
  • A plus is having knowledge of cloud-based workflow managers and analyses
  • Good understanding of capabilities and limits of the modern machine learning techniques and deep learning

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