Senior Research AI Scientist

The Informatics and Predictive Sciences (IPS) mission at Bristol Myers Squibb is to Pioneer, Partner and Predict to drive transformative insights for patient benefit. IPS conducts applied computational research in areas that include genomic, structural and molecular informatics, computational and systems biology, patient selection and translational biomarker research, and broader fields including knowledge science, epidemiology and machine learning - across the full lifecycle of drug discovery and development and across all therapeutic areas at BMS.

Position Summary

We are seeking a Senior Research AI Scientist to partner directly with discovery, translational, clinical, computational, and portfolio research teams to design, evaluate, and operationalize reusable AI-enabled capabilities for high-priority scientific needs. This role sits at the intersection of scientific expertise, applied AI, research informatics, and platform-aligned engineering. The successful candidate will translate complex scientific questions into defensible AI workflows, reusable research tools, rigorous evaluation frameworks, and scientifically grounded agentic systems supporting use cases such as target evidence assembly, indication rationale, biomarker interpretation, translational synthesis, and decision support.

This role calls for a scientist-builder with deep AI fluency and an operating mode characterized by urgency, disciplined iteration, and tight scientific feedback loops. Impact will be measured by the rigor, adoption, reusability, and scientific value of delivered capabilities.

Key Responsibilities

• Partner with discovery, translational, clinical, computational, and portfolio scientists to identify high-value research opportunities for AI and translate them into AI-tractable specifications with measurable scientific success criteria.
• Architect, build, and operationalize agentic and multi-agent workflows for complex scientific tasks such as target evidence assembly, indication rationale construction, biomarker interpretation, translational synthesis, literature and evidence triangulation, and decision support.
• Design reusable AI capabilities, including agentic frameworks, evaluation harnesses, MCP-enabled tool integrations, prompt and policy libraries, and research workflows intended for adoption across multiple programs.
• Establish rigorous evaluation methodologies for AI-enabled research outputs and upstream data/tool quality, including expert-reviewed benchmarks, rubric-based assessments, grounding checks, uncertainty characterization, provenance review, reproducibility criteria, and failure-mode analysis.
• Collaborate with engineering, data, IT, security, legal, vendor, and platform teams to ensure AI capabilities are designed with appropriate governance, integration paths, and credible scaling plans.
• Communicate AI opportunities, risks, limitations, evaluation outcomes, and recommended decisions clearly to scientific, technical, and executive audiences.

Basic Qualifications

• Bachelor's Degree in computational biology, bioinformatics, genetics, biology, chemistry, pharmaceutical sciences, data science, scientific computing, or a closely related field with 7+ years of experience, OR
• Master's Degree in a related field with 5+ years of experience, OR
• PhD in a related field with 2+ years of experience

Preferred Qualifications

• Track record translating complex scientific questions into AI-enabled workflows, reusable tools, evaluation frameworks, data products, or decision-support capabilities.
• Hands-on experience with modern AI and large language model methods, including agentic workflows, multi-agent orchestration, GraphRAG, scaled tool use, and MCP patterns.
• Experience designing and operating scientifically rigorous evaluation frameworks for AI systems (curated benchmark datasets, expert-reviewed reference standards, calibration and uncertainty metrics, regression gating).
• Working fluency in at least one scientific domain relevant to drug R&D (target biology, translational science, computational biology, clinical development, biomarker science).
• Experience with knowledge graphs, biomedical ontologies, evidence/disease/gene-target models, and graph-based reasoning over heterogeneous biomedical evidence.

Compensation (Remote - United States): $128,890 - $156,179. Additional incentive cash and stock opportunities may be available.