Lead Staff Scientist - Advanced Technology Team at Harvard University in Boston, Massachusetts

Posted in Science 12 days ago.

Job Description:

Harvard University

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Lead Staff Scientist - Advanced Technology Team
Harvard Medical School

Job Summary

The Wyss Institute for Biologically Inspired Engineering at Harvard University seeks a talented energetic professional for a leadership position in our Predictive Bioanalytics Initiative.

About the Role:

We are looking for an innovative data scientist with significant expertise in developing and applying machine learning approaches to problems in biology and medicine. Reporting to the Technology Translation Director, you will collaborate with Institute faculty and staff in high-risk, fundamental research and science-driven technology development. In this exciting role, you will be exposed to many different technologies in areas ranging from therapeutics and diagnostics to synthetic biology and materials science. Your goal will be to identify new product solutions and is a unique opportunity to follow your passion either at the Institute or as a future co-founder of a Wyss start-up.

Basic Qualifications

What you'll need:
  • Advanced degree in bioinformatics, computational biology, computer science or related field
  • Minimum of 12 of experience with computational data analysis or related experience. Education may count towards years' experience

Additional Qualifications and Skills

Desirable skills:
  • PhD is strongly preferred.
  • Demonstrated ability to see out collaborations as well as manage and mentor others
  • Experience with omics data analysis - specifically transcriptomics, proteomics and metabolomics data -as well as artificial intelligence and machine learning approaches
  • Proficiency in R, python, or MATLAB. Knowledge of statistical analysis methods like clustering. Fluency with creating and managing large biological databases.
  • Demonstrated success in leading and working in cross-functional teams. Excellent documentation, analytical, communication, and inter-personal skills.
  • Hands on experience systems biology approaches - network inference, statistical learning, and genome-scale modeling a plus. Excellent documentation, analytical, communication, and inter- personal skills.
  • Industrial experience would be beneficial, but is not required