Principal Research AI Innovation Lead

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”- a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits.

This is work that transforms the lives of patients, and the careers of those who do it. When you join BMS, you are joining a high-achieving team united by a common mission. The Informatics and Predictive Sciences (IPS) mission 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  do this in close partnership with scientific and clinical experts in the field, both inside and outside the company.

We perform innovative science to empower key data-driven decisions across a rich pipeline of next-generation medicines. In doing so, our work transforms the lives of patients, as well as our own lives and careers. Here, you’ll get the chance to grow and thrive through opportunities that are uncommon in scale and scope. You’ll pursue innovative ideas while advancing professionally alongside some of the brightest minds in biopharma.

We are seeking a Principal Research AI Innovation Lead to design, prototype, and scale AI-enabled capabilities that accelerate scientific research. This role will work across Research, AI, data, product, engineering, and enterprise technology teams to identify high-value opportunities, build practical LLM-enabled solutions, evaluate scientific quality, and create reusable capability patterns that improve how research teams use AI. The ideal candidate combines hands‑on AI product and prototyping experience with working fluency in drug discovery, translational science, or a related research domain.

They can assess whether an AI output is scientifically sound, appropriately grounded, and useful for real research decisions - not merely technically complete. They are comfortable engaging with scientists on topics such as target evidence, indication selection, biomarker interpretation, translational rationale, or clinical evidence, and equally comfortable partnering with AI engineers to turn those needs into scalable systems. This role’s impact is measured by scientific outcomes: faster and better-evidenced research decisions, higher-quality AI-assisted workflows, and reusable capabilities that compound across programs.

• Partner with scientists and research leaders to identify high-impact opportunities where AI can improve research speed, quality, consistency, traceability, and decision-making.
• Help shape multi-year GenAI strategies, lead work streams, and establish reusable building blocks - agentic frameworks, evaluation harnesses, retrieval and grounding components, tool servers, prompt and policy libraries, and provenance infrastructure - on which research programs build.
• Architect and personally implement the agentic system-of-systems that executes complex, long-horizon scientific workflows across research, including target evidence assembly, indication rationale construction, biomarker interpretation, translational synthesis, literature and evidence…