Data Scientist, Bioengineering
About the role
We need a Data-Scientist who can sit at the interface of Ultrasound, synthetic-biology, and research platforms (imaging, sequencing) and drive the development of rigorous data-analysis pipelines that surface actionable insights for experimental and computational researchers alike. This person will collaborate with domain experts in ML, Ultrasound, synthetic biology, and data-engineering to develop data-analysis pipelines that transform “raw” data into actionable artifacts. This person will subsequently collaborate with ML researchers to develop and run ML pipelines that enable de-novo design and closed-loop active learning cycles across a number of problem domains in bioengineering (delivery, immunology, synthetic biology, protein-engineering).
Responsibilities
- Collaborate with wet-lab scientists to define tractable optimization objectives and metrics, and encode domain specific priors and constraints for downstream computational modeling.
- Stay current with research in Synthetic Biology and ML-guided molecular and cellular engineering, as well as data-analysis methods and techniques for biological data (OMICS, Agentic-workflows).
- Contribute to the long-term research roadmap and serve as a thought-leader for scientists.
Requirements
- Deep grounding in synthetic biology, molecular engineering, and computational methods for data-analysis.
- Experience in biomolecular ultrasound.
- Familiarity with fundamental concepts in NGS, Omics, ML, and molecular engineering.
- Proficiency in Python / PyTorch / BoTorch / Pyro and comfort writing clean, reproducible production grade code.
- Experience bridging machine learning and experimental science, especially working with sparse, noisy, and or high-cost data.
Pay
Compensation Range: $120K - $200K