Senior Scientist, AI Computational Structural Biology

The Informatics and Predictive Sciences (IPS) mission is to Pioneer, Partner and Predict to drive transformative insights for patient benefit. IPS conducts applied computational research in areas that include genomic, structural and molecular informatics, computational and systems biology, patient selection and translational biomarker research, and broader fields including knowledge science, epidemiology and machine learning—across the full lifecycle of drug discovery and development and across all therapeutic areas at BMS.

This is a unique opportunity to shape the next generation of AI-driven drug discovery at BMS, with direct impact on our induced proximity and targeted protein degradation programs. We are seeking a collaborative and highly innovative computational biologist with a strong background in AI co-folding modeling and deep understanding of protein structures to join our Predictive Structure and Function team. They will work alongside scientists in Protein Homeostasis, Computational Sciences, Oncology and other teams in the IPS department to discover novel target opportunities leveraging protein-protein interactions and apply learnings to other small and large molecule modalities.

Responsibilities

• 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 & 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 contributing to the generation of novel druggability hypotheses for hard-to-drug targets.
• Author scientific reports, and present methods, results, and conclusions to publishable standard.
• Contribute to the planning and execution of collaborative projects with leading academic and commercial research groups worldwide.

Basic Qualifications

• Bachelor's Degree with 7+ years of academic/industry experience, OR Master's Degree with 5+ years, OR PhD with 2+ years of academic/industry experience.

Preferred Qualifications

• Ph.D. 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, with 2+ years of post-doctoral experience developing AI/ML approaches in university, pharma, or biotech settings focusing on drug discovery.

Requirements - Technical Skills & Experience

• AI/ML & Deep Learning: Proven experience developing and deploying AI/ML models in biological or biochemical contexts, with proficiency in deep learning architectures for structural data (GNNs, equivariant neural networks, transformers, diffusion, flow matching) using Python and relevant libraries (PyTorch, JAX, RDKit, ESM/fair-esm, Biopython, etc.).
• Structural Biology and Co-folding: Deep expertise in protein-protein interactions, protein folding, and biomolecular complex formation, with hands-on experience in structure prediction and co-folding frameworks (e.g. AlphaFold2, AlphaFold-Multimer, RoseTTAFold2, Boltz, Chai-1, NeuraPlexer, or equivalent).

Preferred Skills

• Multi-Omics Data Integration: Ability to integrate and analyze multimodal datasets—structural, genomic, and proteomic—from public and proprietary sources.
• Chemoproteomics & induced proximity: Familiarity with chemoproteomics data for druggability/ligandability assessment; experience with induced proximity modalities (PROTACs, molecular glues, bifunctional molecules) is appreciated but not required.

Compensation: Cambridge Crossing, MA: $148,210 - $179,601. San Diego, CA: $141,770 - $171,794. Additional incentive cash and stock opportunities (based on eligibility) may be available.