Machine Learning Scientist, Perturbational Biology
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.
Are you excited to model cellular-resolution data to understand the heterogeneous impact of perturbations on complex biological systems? To jointly optimize both models and data generation at scale to drive a new and far-reaching drug discovery paradigm?
At Cellarity, you’ll join a team of machine learning scientists and engineers, developing a general platform that uncovers and targets the causes of disease at the level of cellular behavior.
You'll also collaborate with computational biologists and biologists on disease biology, computational chemists on molecular optimization, and lab scientists on generating groundbreaking data assets.
If you’re eager to grow as a multi-lingual scientist, while pioneering the data, models, and process for cellular-resolution drug discovery, then we’re eager to hear from you.
- Lead and collaborate on the development of computational models of the heterogeneous impact of small molecules on complex biological systems, with an initial focus on transcriptional readouts.
- Use machine learning to inform our data generation strategy to enable new capabilities and meaningful benchmarks.
- Integrate clean implementations into the platform and iterate based on feedback from their application across many diverse disease programs.
- Demonstrate rigorous data science, efficient proofs-of-concept, and nimble iteration, as well as clear thinking about ambitious goals and how to break down and quantify progress.
- Clearly document and present your work to computational colleagues, as well as to interdisciplinary teams of biologists, technologists, and executives at company meetings. This requires continuous teaching, learning, and engagement with the bigger picture for cohesive impact.
- Ph.D in computational biology, computer science or related scientific discipline, with significant experience developing and applying computational models to cellular data.
- Demonstrated scientific understanding of and computational fluency with single-cell expression data.
- Solid foundations in applied math and machine learning to digest computational research from inside and outside of biology.
- Strong programming and scripting skills, preferably in Python, including experience with version control, documentation, testing, and the data science ecosystem.
- Strong written and oral communication skills at both a technical and scientific level.
- Ability to work both independently and as part of a team.
- Experience developing and applying models of bulk and single-cell expression data, under varying conditions.
- Experience with other biological (epigenetic, protein, imaging, …), genomic, and medical data types.
- Chemistry background, experience modeling chemical structure.
- Experience with optimal experimental design, Bayesian statistics, causal inference, dynamical systems, domain adaptation / transfer learning, deep generative models, manifold learning, or geometric deep learning (learning on graphs and/or manifolds).
- Experience with PyTorch, cloud computing, and hardware acceleration.
- Interest in learning any of the above.
Location: Cambridge, MA
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