Senior Specialist, Data Science & AI
Core
Develop, evaluate, and apply AI methods and workflows for low molecular weight (LMW) drug discovery, including hit generation, lead optimization, and pre-clinical analysis.
Role type
Senior Applied AI Scientist (Drug Discovery)
Builds
AI models and workflows for molecular design, property prediction, and drug discovery cycles
Domain
Biopharmaceuticals / Computational Chemistry
Deliverable
production ML models
Required skills
Python, deep learning frameworks, generative chemistry, structure-based drug design, protein-ligand modelling, QSAR, multi-objective property optimization, molecular representation learning, graph neural networks, foundation models for chemistry
Preferred skills
experience in large research organizations, uncertainty quantification, active learning, agentic AI, counterfactuals, explainability
Technologies
Python, GitHub, git, subversion, bitbucket
Responsibilities
Contribute to conceptualization of AI methods for hit generation and lead optimization; Develop and evaluate state-of-the-art AI/machine learning models including foundation models and generative AI; Establish rigorous evaluation and benchmarking frameworks; Define translatable metrics connecting model performance to scientific decisions; Integrate AI approaches into design-make-test-analyze (DMTA) cycles; Collaborate cross-functionally with medicinal chemists, DMPK, and structural biology teams
Seniority
Senior, hands-on IC
