Associate Director, Commercial AI Product Owner

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.

Summary:

As Associate Director, Commercial AI Product, you will lead the transformation of commercial business processes into forms that AI can understand and act on. This role ensures that the way brands and commercial teams work feeds into BMS's enterprise AI solutions. By owning the unstructured content and its relationships in the next-generation knowledge and context layer, a new generation of machine-readable business information, you will make AI more grounded, accurate and context-aware in supporting commercial decision-making.

Building on this foundation, you will work closely with brand, IT and commercial analytics teams to make AI smarter, moving it beyond descriptive to diagnostic, predictive and prescriptive by using context and machine learning-driven solutions. With a strong understanding of context engineering and ML operations, the right candidate acts as a cross-functional team player who helps boost the AI product to a new level of intelligence.

You will translate BMS Commercial AI, Analytics & Insights product team’s vision and roadmap into business technical requirements, actionable tasks, milestones, and clear outcomes.

Responsibilities:

Roadmap Execution & Context Engineering

• Roadmap Execution: Translate the Commercial AI, Analytics & Insights product team's vision and roadmap into business technical requirements, actionable tasks, milestones and clear outcomes.
• Knowledge & Context Engineering: Translate business knowledge into the context layer through context design: generate and connect to unstructured content as knowledge assets, build its relationships, use a graph database, and perform lifecycle management for enterprise knowledge assets. Make sure skills have the right guidance to connect to the knowledge graph, and that the knowledge graph is used in our key AI products across brands.
• Brand & Stakeholder Interface: Work directly with the brand teams on the initial development of the knowledge graph in the context layer, brand by brand. After
• initial deployment, work with the brand pods and enablement team to continually improve the knowledge layer in the back end of the AI product.

ML Operations & AI Performance Optimization

• ML Operation: Implement the right ML framework in the AI product to use ML capability and push AI beyond descriptive analysis. Work closely with the IT team to monitor ML performance, and work with CAAI analytics leads to improve ML performance as needed.
• AI Performance Optimization (Golden Q&A): Design, build and maintain a golden Q&A evaluation framework for continuous AI performance monitoring. The golden Q&A set is a curated, business-validated collection of representative questions with reference answers. Use a held-out (out-of-training) test set to prevent data leakage and enable unbiased benchmarking. Evaluate both structured and unstructured outputs against defined metrics and thresholds, such as accuracy, groundedness, faithfulness and relevance.
• Business Acceptance & UAT: Define test scope and context test cases for UAT, drawing on the golden Q&A set, and resolve context-layer defects.
• Performance Metrics: Track context-layer usage and quality signals and provide them as performance metrics.

Collaboration, Governance & Trust

• Offshore Team Collaboration: Work with the Offshore Sr. Manager and AOI Ops on validation of the knowledge and context layer and the feedback loop from the Ops roles.
• Responsible AI & Governance: Partner with Data Governance, Legal and Privacy on privacy, explainability and human-in-the-loop checks for knowledge and context content. Support the AI product team in keeping the governance forums and technical controls.

Expected Outcomes

• Releases meet business acceptance criteria and fit brand workflows, and the roadmap is delivered as working context-layer capabilities.
• Context content supports a consistent experience across brands.
• The knowledge and context layer is used in CAAI AI products and improves from business feedback.
• Context-layer usage and quality are measurable, and the frameworks are reusable across enterprise solutions.
• A clear context-engineering backlog built from brand team feedback and aligned with the CAAI product team roadmap and guidance.
• A well-architected ML model catalog, with standardized metadata, versioning, lineage and performance documentation, that the AI product can discover and invoke programmatically.

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