Principal Scientist, AI-Driven Oncology Target Discovery
Bristol Myers Squibb
2 months ago
Remote friendly (Cambridge, MA)
United States
Clinical Research and Development
Summary
Principal Computational Scientist (machine learning, agentic AI, oncology discovery) to identify novel therapeutic targets and resistance mechanisms.
Key Responsibilities
- Integrate computational approaches into cross-functional target discovery workflows to inform target nomination and portfolio decisions.
- Design and deploy agentic AI and machine learning systems for end-to-end target identification, prioritization, and validation.
- Integrate and analyze multimodal patient-derived datasets (omics, functional screens, real-world data) to uncover disease biology, resistance mechanisms, and patient stratification.
- Convert complex analyses into decision-ready insights for Go/No-Go recommendations and target discovery strategy.
- Author scientific reports and present methods/results to a publishable standard.
Basic Qualifications
- Bachelor’s degree + 8+ years academic/industry experience; or Master’s + 6+ years; or PhD + 4+ years.
Preferred Qualifications / Required Skills
- PhD in Computational Biology, Systems Biology, Computer Science, Machine Learning, Statistics, or related quantitative field.
- Expertise in computational target identification (functional genomics, perturbation screens, single-cell/spatial omics, real-world patient data).
- Experience developing/deploying applied AI agents and/or LLM-driven applications.
- Collaboration/leadership in fast-paced, ambiguous environments.
- Systems biology/disease biology grounding in drug discovery (preferably oncology) and translating computational findings into therapeutic hypotheses.
- Publication record and ability to communicate complex methods.
Compensation & Benefits (as stated)
- Cambridge Crossing: $166,770–$202,086.
- Health, wellbeing, and financial protection benefits; 401(k), disability, life/accident insurance, etc.
- Paid time off: flexible/unlimited (US exempt) or 160 hours annual vacation (Phoenix/Puerto Rico/Rayzebio exempt/non-exempt/hourly), plus national holidays.
Principal Computational Scientist (machine learning, agentic AI, oncology discovery) to identify novel therapeutic targets and resistance mechanisms.
Key Responsibilities
- Integrate computational approaches into cross-functional target discovery workflows to inform target nomination and portfolio decisions.
- Design and deploy agentic AI and machine learning systems for end-to-end target identification, prioritization, and validation.
- Integrate and analyze multimodal patient-derived datasets (omics, functional screens, real-world data) to uncover disease biology, resistance mechanisms, and patient stratification.
- Convert complex analyses into decision-ready insights for Go/No-Go recommendations and target discovery strategy.
- Author scientific reports and present methods/results to a publishable standard.
Basic Qualifications
- Bachelor’s degree + 8+ years academic/industry experience; or Master’s + 6+ years; or PhD + 4+ years.
Preferred Qualifications / Required Skills
- PhD in Computational Biology, Systems Biology, Computer Science, Machine Learning, Statistics, or related quantitative field.
- Expertise in computational target identification (functional genomics, perturbation screens, single-cell/spatial omics, real-world patient data).
- Experience developing/deploying applied AI agents and/or LLM-driven applications.
- Collaboration/leadership in fast-paced, ambiguous environments.
- Systems biology/disease biology grounding in drug discovery (preferably oncology) and translating computational findings into therapeutic hypotheses.
- Publication record and ability to communicate complex methods.
Compensation & Benefits (as stated)
- Cambridge Crossing: $166,770–$202,086.
- Health, wellbeing, and financial protection benefits; 401(k), disability, life/accident insurance, etc.
- Paid time off: flexible/unlimited (US exempt) or 160 hours annual vacation (Phoenix/Puerto Rico/Rayzebio exempt/non-exempt/hourly), plus national holidays.