Lead AI Scientist, Oncology Target Discovery

Biopharma Careers; Virginia (MN); On-site; USD 167,000 - 202,000; Full time

Benefits offered by this job

Health Coverage

Wellbeing Support

Financial well-being and protection

Job summary

Bristol Myers Squibb is seeking a highly motivated Principal Computational Scientist at the intersection of machine learning, agentic AI, and oncology discovery to drive therapeutic target identification and resistance mechanism insights.

You will integrate multimodal datasets to nominate and validate targets, design scalable analytical platforms, and work with cross-functional teams to accelerate decision-making across discovery pipelines.

Qualifications

• Bachelor's Degree with 8+ years academic/industry experience
• Master's Degree with 6+ years academic/industry experience
• PhD with 4+ years academic/industry experience
• Ph.D. in Computational Biology, Systems Biology, CS, ML, Statistics or related field
• Experience deploying AI agents and LLMs in research or industry

Responsibilities

• Drive integration of computational approaches into cross-functional target discovery workflows.
• Lead design and deployment of agentic AI and ML systems for target identification, prioritization, validation.
• Analyze multimodal datasets to uncover disease biology and inform target selection.
• Translate analytical outputs into clear, decision-ready insights for Go/No-Go decisions.
• Author reports and present methods and conclusions to a publishable standard

Skills

Agentic AI

Cross-functional collaboration

Leadership

Machine learning

Data analysis

Education

Bachelor's degree

Master's degree

PhD

Tools

Python

R

Job description

Bristol Myers Squibb is seeking a highly motivated Principal Computational Scientist at the intersection of machine learning, agentic AI, and oncology discovery to drive therapeutic target identification and resistance mechanism insights.

You will integrate multimodal datasets to nominate and validate targets, design scalable analytical platforms, and work with cross-functional teams to accelerate decision-making across discovery pipelines.