Computational Biologist, Therapeutics R&D

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What if you could join a rapidly growing company and play a critical role in bringing new medicines to patients through looking at and treating disease in a revolutionary way.

Founded by Flagship Pioneering, Cellarity is the first company developing medicines through an understanding of cell behaviors. The company’s broad platform harnesses single-cell technologies and machine learning to digitize and quantify cellular behaviors, unravel the network dynamics that govern those behaviors, and generate medicines that can direct them. Cellarity is using its platform to design medicines targeting the full cellular and molecular complexity of disease, enabling a quantum leap in the success rate and speed of drug discovery, design and development. For additional information, visit www.Cellarity.com.

Summary:

Are you a highly motivated and organized computational scientist who is enthusiastic about developing, learning, and applying your computational skills to understand complex biological systems?

You’ll get the opportunity to work in an innovative computational team, driven to deliver high-impact results. You will be in an early group of pioneers developing the world's first AI platform that unveils the causes of emergent disease based on cellular understanding.

You will begin your career at Cellarity with a slew of world-class computational biologists and machine learning scientists (https://cellarity.com/the-team), biologists (co-developing hypotheses), chemists, clinicians, and technologists (co-developing proprietary data assets).

If you think you can contribute to any of these aspects/capabilities that we are building, and are keen on testing your hypotheses and learning from some of the best scientists, whilst getting to work with proprietary and relevant data sets, then we are looking for you.

Key responsibilities:

  • You develop and apply computational biology and machine learning tools to generate insights and hypotheses from high-dimensional molecular datasets, with a focus on scRNA-seq & snRNA-seq paired with phenotypic readouts.
  • You develop data analysis strategies and lead computational analyses on diverse biological and clinically relevant projects.
  • You collaborate closely with experimental scientists from various teams and ensure that data is interrogated to deliver high value readouts.
  • You contribute to experimental study designs that allow to measure what we predict.
  • You present your results in an interdisciplinary team of biologists, chemists, clinicians, technologists, and other machine learning colleagues in meetings varying covering cross-functional project teams, functional teams, to whole company and management meetings.

Minimum qualifications:

  • Ph.D. or Master's degree in biology, computational biology, or related scientific field. Advanced degrees in orthogonal fields such as physics & mathematics field are very much welcome, but you would need to have some experience in a biological field.
  • Demonstrated scientific understanding of molecular and systems biology, diverse molecular data types, and analysis tools.
  • Practical programming and scripting skills, preferably in Python.
  • Fast learner, analytical thinker, creative, "hands-on", strong communication skills.
  • Able to work both independently and as part of a team.
  • We would like to see your GitHub repository or papers that can showcase this. If you are from biotech/pharma, we would ask you to share (where you can), your experiences with a focus on your role and what you specifically contributed to in a program.

Bonus:

  • Experience with biological, medical, chemical data
  • Experience with emergent behavior in complex systems, time series analysis, causal inference, domain adaptation, transfer learning, multi-modal deep learning, geometric deep learning (learning on graphs and/or manifolds)
  • Ability to Google error messages and seek resolution from self-investigation and/or get advice from the rest of the crew
  • Interested in learning any of the above
 
 
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