Principal Scientist, Computational Drug Design

Bristol Myers Squibb; San Diego (CA); On-site; USD 157,000 - 190,000; Full time

Benefits offered by this job

Wellbeing support

Flexible Time Off

Company holidays

Job summary

Bristol Myers Squibb in San Diego is seeking a Principal Scientist for the Small Molecule Computational Design team. You will blend structure-based design with data-driven approaches to translate molecular hypotheses into actionable designs that advance discovery programs.

Lead and mentor teams, integrate structural data with large-scale analyses, curate data resources, and drive predictive modeling and cheminformatics workflows across projects.

Qualifications

• Ph.D. with 4+ years of academic or industry experience, Master’s degree with 6+ years of experience, Bachelor’s degree with 8+ years of experience in computational chemistry, cheminformatics, computational biophysics, chemistry, or a related discipline.
• Strong history of directly impacting small molecule discovery programs in biotech or pharmaceutical industry, with a solid working knowledge of medicinal chemistry principles.
• Demonstrated hands-on depth across both computer-aided drug design and cheminformatics/machine learning.
• Experience with targeted protein degradation, molecular glues, or other induced-proximity modalities — including ternary complex modeling, cooperativity assessment, or E3 ligase chemical space — is preferred.
• Demonstrated experience applying computational methods to a variety of biological targets (e.g., proteins, DNA, RNA) and familiarity with diverse therapeutic modalities such as covalent and non-covalent inhibitors, allosteric modulators, targeted protein degraders, antibodies, and antibody-drug conjugates.

Responsibilities

• Serve independently as the computational scientist on Protein Homeostasis project teams, bringing both structure-based and data-driven approaches to bear on the same problem, and holding accountability for delivery of the computational objectives of those programs.
• Design, prioritize, and triage degrader and molecular glue chemical matter, applying multi-parameter optimization to advance potency, selectivity, ternary complex formation and cooperativity, degradation efficiency, and ADME properties, while proactively identifying and mitigating potential toxicological risks.
• Develop, validate, and deploy predictive models and cheminformatics workflows and drive their adoption by project teams as routine decision-making tools rather than one-off analyses.
• Integrate structural information from co-crystal structures, cryo-EM, and predicted or modeled structures with large-scale data analysis and visualization to deepen structure-activity relationship insight and shape project strategy.
• Curate and steward the chemical and biological data resources, reusable tooling, and analysis infrastructure that the group depends on, ensuring analyses are reproducible, discoverable, and useful to scientists beyond their original author.
• Provide scientific leadership within Computational Sciences and our close partners on emerging technologies, platforms, targets, etc., taking calculated risk to test and advance innovative approaches within the group and among stakeholders
• Act as an active mentor to colleagues, serve as a recognized internal subject area resource, and grow internal and external scientific visibility through publications, presentations, and patent applications.

Skills

Computational chemistry

Cheminformatics

Machine learning

Structure-based design

Molecular design

Protein Homeostasis

Education

PhD

Master's degree

Bachelor's degree

Tools

Molecular modeling

Predictive modeling

Job description

Bristol Myers Squibb in San Diego is seeking a Principal Scientist for the Small Molecule Computational Design team. You will blend structure-based design with data-driven approaches to translate molecular hypotheses into actionable designs that advance discovery programs.

Lead and mentor teams, integrate structural data with large-scale analyses, curate data resources, and drive predictive modeling and cheminformatics workflows across projects.