Principal Scientist – Computational Sciences Full-time

Principal Scientist – Computational Sciences

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.

Position Summary

Computational Sciences is seeking an exceptional Principal Scientist to join our Small Molecule Computational Design team. Using computational chemistry, cheminformatics, and machine learning, you will work as a member of our multidisciplinary Protein Homeostasis project teams to proactively generate and test scientific hypotheses and directly translate those into actionable molecular designs that meaningfully advance our discovery programs. We are looking for a scientist who is equally excited about building a structural model as they are to build the machine learning model, dataset, or analysis that influences how an entire program reasons about its chemistry. If you thought you had to choose between structure-based design and cheminformatics and would rather not, this position was written for you.

What You'll Do

• 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.

What We're Looking For

We're seeking an experienced, innovative scientist that passionate about the role of computation in drug design. The ideal candidate will bring:

• 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. This dual capability is the defining requirement of the role.
• 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.
• Superior communication skills and a collaborative mindset that thrives in multidisciplinary and geographically disparate project teams.
• Track record of publications in peer-reviewed scientific journals.