Principal Scientist, Computational Sciences - Protein Structure Prediction and Design
Bristol Myers Squibb
9 hours ago
Remote friendly (Cambridge, MA)
United States
Clinical Research and Development
Key Responsibilities:
- Develop and scale antibody design capabilities from prototype to application; advance agentic antibody design approaches into robust, reusable workflows for preclinical discovery.
- Build and apply state-of-the-art models for biologics design: protein structure modeling, binder design, affinity/specificity prediction, and developability property prediction using internal and external datasets.
- Deliver production-ready research tools: end-to-end ownership of computational pipelines with reproducibility, benchmarking, and maintainable, well-documented code.
- Lead through influence: partner with computational and wet-lab teams to prioritize capabilities, translate insights into actionable decisions, and communicate clearly to technical and non-technical audiences.
Required Qualifications:
- Ph.D. in structural bioinformatics, computational biology, computer science, engineering, physics, or related; 4+ years relevant industry/academic experience.
- Modern ML expertise (e.g., transformers; diffusion/flow-based generative models) plus classical ML fundamentals.
- Experience developing/evaluating predictive models, including assessment, benchmarking, and experimental design.
- Hands-on protein modeling (protein structure prediction and generative protein design).
- Experience with agentic AI frameworks for applications automating and accelerating research workflows.
- Strong Python skills; commitment to reproducible research and high-quality scientific software.
- Ability to identify high-impact problems and drive solutions through implementation and evaluation.
- Cross-disciplinary collaboration; communicate findings via visualization, concise narratives, and actionable recommendations.
Preferred Qualifications:
- Physics-based modeling (e.g., MD, free energy perturbation) or closed-loop optimization (e.g., Bayesian optimization, active learning).
- Background in biochemistry, protein engineering, or related experimental disciplines.
Benefits (explicit): Health coverage (medical, pharmacy, dental, vision), wellbeing support programs, 401(k), disability/life/accident insurance and related protections; Paid Time Off (flexible time off for US exempt; 160 hours annual paid vacation + holidays for Phoenix/Puerto Rico/Rayzebio exempt/non-exempt/hourly).
- Develop and scale antibody design capabilities from prototype to application; advance agentic antibody design approaches into robust, reusable workflows for preclinical discovery.
- Build and apply state-of-the-art models for biologics design: protein structure modeling, binder design, affinity/specificity prediction, and developability property prediction using internal and external datasets.
- Deliver production-ready research tools: end-to-end ownership of computational pipelines with reproducibility, benchmarking, and maintainable, well-documented code.
- Lead through influence: partner with computational and wet-lab teams to prioritize capabilities, translate insights into actionable decisions, and communicate clearly to technical and non-technical audiences.
Required Qualifications:
- Ph.D. in structural bioinformatics, computational biology, computer science, engineering, physics, or related; 4+ years relevant industry/academic experience.
- Modern ML expertise (e.g., transformers; diffusion/flow-based generative models) plus classical ML fundamentals.
- Experience developing/evaluating predictive models, including assessment, benchmarking, and experimental design.
- Hands-on protein modeling (protein structure prediction and generative protein design).
- Experience with agentic AI frameworks for applications automating and accelerating research workflows.
- Strong Python skills; commitment to reproducible research and high-quality scientific software.
- Ability to identify high-impact problems and drive solutions through implementation and evaluation.
- Cross-disciplinary collaboration; communicate findings via visualization, concise narratives, and actionable recommendations.
Preferred Qualifications:
- Physics-based modeling (e.g., MD, free energy perturbation) or closed-loop optimization (e.g., Bayesian optimization, active learning).
- Background in biochemistry, protein engineering, or related experimental disciplines.
Benefits (explicit): Health coverage (medical, pharmacy, dental, vision), wellbeing support programs, 401(k), disability/life/accident insurance and related protections; Paid Time Off (flexible time off for US exempt; 160 hours annual paid vacation + holidays for Phoenix/Puerto Rico/Rayzebio exempt/non-exempt/hourly).