Principal Scientist, Computational Sciences - Protein Structure Prediction and Design
Bristol Myers Squibb
8 hours ago
Remote friendly (Cambridge Crossing, FL)
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 protein structure modeling, binder design, affinity/specificity prediction, and developability property prediction using internal and external datasets.
- Own end-to-end development of computational pipelines with reproducibility, benchmarking, and maintainable, well-documented code.
- 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 field + 4+ years relevant industry/academic experience.
- Expertise in modern ML (e.g., transformers; diffusion/flow generative models) and strong classical ML fundamentals.
- Experience developing/evaluating predictive models; familiarity with model assessment, benchmarking, and experimental design.
- Hands-on protein modeling (state-of-the-art protein structure prediction and generative protein design).
- Experience with agentic AI frameworks to automate and accelerate research workflows.
- Strong Python skills; commitment to reproducible research and high-quality scientific software.
- Ability to identify high-impact problems; work independently; implement and evaluate solutions.
- Clear cross-disciplinary communication via visualization and actionable recommendations.
Preferred Qualifications
- Physics-based modeling (e.g., molecular dynamics, free energy perturbation) or closed-loop optimization (e.g., Bayesian optimization, active learning).
- Background in biochemistry, protein engineering, or related experimental disciplines.
Compensation/Benefits (highlights)
- US $156,890–$202,086 (location-dependent); additional incentive cash/stock may be available.
- Health coverage, wellbeing support, 401(k), disability/life insurance; paid time off.
Application Instructions
- If the role doesn’t perfectly match your resume, they encourage you to apply anyway.
- 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 protein structure modeling, binder design, affinity/specificity prediction, and developability property prediction using internal and external datasets.
- Own end-to-end development of computational pipelines with reproducibility, benchmarking, and maintainable, well-documented code.
- 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 field + 4+ years relevant industry/academic experience.
- Expertise in modern ML (e.g., transformers; diffusion/flow generative models) and strong classical ML fundamentals.
- Experience developing/evaluating predictive models; familiarity with model assessment, benchmarking, and experimental design.
- Hands-on protein modeling (state-of-the-art protein structure prediction and generative protein design).
- Experience with agentic AI frameworks to automate and accelerate research workflows.
- Strong Python skills; commitment to reproducible research and high-quality scientific software.
- Ability to identify high-impact problems; work independently; implement and evaluate solutions.
- Clear cross-disciplinary communication via visualization and actionable recommendations.
Preferred Qualifications
- Physics-based modeling (e.g., molecular dynamics, free energy perturbation) or closed-loop optimization (e.g., Bayesian optimization, active learning).
- Background in biochemistry, protein engineering, or related experimental disciplines.
Compensation/Benefits (highlights)
- US $156,890–$202,086 (location-dependent); additional incentive cash/stock may be available.
- Health coverage, wellbeing support, 401(k), disability/life insurance; paid time off.
Application Instructions
- If the role doesn’t perfectly match your resume, they encourage you to apply anyway.