Develop ML/AI models that support discovery workflows, including target prioritization, multi‑omics integration, and mechanistic inference.
Apply modern ML approaches (e.g., deep learning, graph learning, foundation models, generative models) to chemical, biological, imaging, and assay datasets.
Build and optimize models for real‑world R&D use cases, ensuring scalability, interpretability, and scientific rigor.
Design, build, and maintain robust data pipelines that curate, standardize, and integrate diverse R&D datasets (chemical, biological, multi‑omics, imaging, biophysical, automation logs, etc.)
Partner with platform teams to implement best‑practice MLOps/DevOps workflows and deploy ML models into production R&D environments.
Work hand‑in‑hand with TD scientists to understand key biological and chemical questions and shape computational strategy accordingly.
Translate sparse, heterogeneous experimental datasets into insights that guide decision‑making in hit discovery, mechanism studies, perturbation experiments, and compound optimization.
Participate in design, interpretation, and iterative refinement of discovery experiments.
Partner with cross-functional teams in R&D Data Science, IT, platform engineering, and therapeutic area groups to drive AI/ML adoption.
Contribute to evaluating new analytical methods, automation technologies, and data platforms supporting next‑generation discovery science.

Master’s or Ph.D. in Computational Biology, Bioinformatics, Data Science, Chemistry, Chemical Biology, Biomedical Engineering, Computer Science, or related field
Experience applying ML/AI in scientific domains (drug discovery, biology, chemistry, systems biology, imaging, or related areas)
Strong programming skills in Python (preferred) and experience with scientific/ML libraries (PyTorch, TensorFlow, scikit‑learn, RDKit, etc.)
Practical experience with data engineering, including data modeling, workflow orchestration, ETL/ELT pipelines, and cloud computing environments (AWS, GCP, or Azure)
Ability to work directly with experimental scientists to solve real R&D challenges.

Principal Data Scientist – R&D DSDH, Therapeutics Discovery

Key skills