Senior Scientist, AI Computational Structural Biology

Senior Scientist, AI Structural Biology

Join BMS as a Senior Scientist in AI Structural Biology within the Informatics & Predictive Sciences (IPS) department. You will help shape AI-driven drug discovery and support our induced proximity and targeted protein degradation programs.

• Develop AI/ML models to predict structural biomolecular interactions for novel modalities leveraging protein–protein interactions.
• Design innovative AI/ML approaches to predict protein cooperativity, affinity, and other biological properties based on structures.
• Develop co‑folding models that incorporate structural and non‑structural priors using combinations of public and proprietary BMS data.
• Develop AI/ML models that leverage chemoproteomics data to generate novel druggability hypotheses for hard‑to‑drug targets.
• Author scientific reports and present methods, results, and conclusions at a publishable standard.
• Contribute to the planning and execution of collaborative projects with leading academic and commercial research groups worldwide.

• Bachelor’s Degree with 7+ years of academic/industry experience
• Master’s Degree with 5+ years of academic/industry experience
• PhD with 2+ years of academic/industry experience

• PhD in Computational Biology, Bioinformatics, Structural Biology, Computer Science, Chemistry, or a closely related field, with a strong focus on AI/ML applications in drug discovery and 2+ years of post‑doctoral experience.

• AI/ML & Deep Learning:
  Proven experience developing and deploying AI/ML models in biological contexts, proficiency with deep learning architectures for structural data (GNNs, equivariant neural networks, transformers, diffusion, flow matching) using Python and libraries such as PyTorch, JAX, RDKit, ESM/fair-esm, Biopython.
• Structural Biology and Co‑folding:
  Deep expertise in protein–protein interactions, protein folding, biomolecular complex formation, and hands‑on experience with structure prediction and co‑folding frameworks (e.g., AlphaFold2, Alpha Fold‑Multimer, RoseTTAFold2, Boltz, Chai‑1, Neura Plexer).
• Multi‑Omics Data Integration:
  Ability to integrate and analyze multimodal datasets—structural, genomic, proteomic—from public and proprietary sources, with experience developing algorithms for genomics and proteomics data.
• Chemoproteomics & Induced Proximity:
  Familiarity with chemoproteomics data for druggability and ligandability assessment; experience with induced proximity modalities (PROTACs, molecular glues, bifunctional molecules) is appreciated.

• Strong problem‑solving mindset and ability to design novel computational approaches for challenging biological questions.
• Experience leading or contributing to collaborative research projects with academic and/or industry partners and translating findings into actionable drug discovery insights.
• Self‑motivated with strong organizational skills, capable of managing multiple projects simultaneously.
• Demonstrated ability to present complex methods and results to diverse scientific audiences and author high‑quality scientific reports and publications to publishable standard.

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