As a Data Scientist in AI Drug Discovery, you will use data science and machine learning to help answer consequential questions in drug discovery: which targets to pursue, which perturbations to test, and how cellular systems may respond.
- Develop and validate models. Build, train, benchmark and validate machine learning models for scientific data, including single-cell omics, genetics, chemistry and imaging.
- Advance virtual cell modelling. Develop and evaluate models that predict cellular state and response to genetic or chemical perturbations.
- Support target identification. Combine model outputs, perturbation evidence, and prior biological knowledge to nominate and rank candidate targets.
- Create analysis-ready data. Build reproducible workflows to ingest, harmonise, quality-control and document public and internal datasets across formats and modalities.
- Integrate scientific knowledge. Incorporate regulatory and interaction networks, ontologies, pathways and other biological or chemical priors into models, and quantify the contribution of each information source.
- Master’s degree in Machine Learning, Computer Science, Computational Chemistry, Computational Biology, Physics, Statistics, or a closely related quantitative field
- Nice to have: Perturb-seq
- Nice to have: CRISPR screens
- Nice to have: virtual cell models
- Nice to have: foundation models
Data Scientist -AI Drug DiscoveryLilly · Bangalore, India