Senior Manager, Research Intelligence Engineer

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.

Bristol Myers Squibb seeks a hands-on, technically accomplished Senior Manager to advance Research Intelligence & data capabilities within Molecular Invention Data | R&D Data & Analytical Platforms BI&T.

• Partner with scientists, portfolio teams, product owners, and operations leaders to frame ambiguous business and scientific questions as actionable intelligence opportunities.
• Develop sufficient understanding of Research domains, portfolio processes, decision points, data semantics, and user needs to guide solution design.
• Lead discovery, requirements analysis, process decomposition, and Process Context Mapping for AI-enabled and data-driven workflows.
• Take the lead in analyzing significantly complex, unscoped Research data, workflow, and operational problems; identify business opportunities and areas of operational whitespace where data, automation, or agentic capabilities can create measurable value rather than waiting for opportunities to be predefined.
• Translate domain needs into prioritized use cases, product requirements, data requirements, acceptance criteria, and measurable outcomes.

• Design, prototype, engineer, and product ionize agentic and generative AI capabilities that support Research portfolio management, operational excellence, and scientific decision-making.
• Build AI-enabled workflows that combine enterprise data, domain context, retrieval, tools, APIs, models, and human decision points.
• Implement evaluation, testing, observability, guardrails, and feedback mechanisms for reliable and responsible AI behavior.
• Create reusable patterns, components, and technical documentation that accelerate adoption across Research tools and data.

• Engineer and integrate the data services, semantic context, metadata, APIs, and platform components required for Research Intelligence solutions.
• Develop strong hands-on expertise in Databricks as the organization’s data intelligence platform, and work across Research Central, Research data products, source systems, and enterprise tooling to deliver secure, maintainable end-to-end solutions.
• Apply sound software and data engineering practices, including modular design, version control, automated testing, CI/CD, monitoring, and production support.
• Partner with data architecture, data engineering, platform, security, and governance teams to align solutions with enterprise standards and FAIR data principles.

• Own value work streams from discovery and architecture through iterative delivery, validation, release, adoption, and operational support.
• Develop roadmaps and delivery plans that balance near-term experimentation with scalable, supportable enterprise capability.
• Define success measures and use evidence from adoption, quality, cycle time, user feedback, and decision impact to improve solutions.
• Identify delivery risks, dependencies, and data or process gaps early, and coordinate resolution across teams.
• Support change, documentation, training, and adoption so that technical capabilities become sustained Research practices.

• Serve as a…