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 Owner, you will be responsible for leading the transformation of advanced analytics into durable, scalable, and AI-powered solutions that directly accelerate commercial decision-making across the enterprise.

You will collaborate across global teams—including BI&T, Data Science, and the engineering/analytics group—to deliver high-quality, production-grade analytics platforms, tools, and automation that embed actionable intelligence into everyday workflows for Marketing, Field, and Market Access stakeholders. This role enables the shift from project-based analytics to platform-enabled, always-on decision intelligence.

You will operate within a hub-and-spoke model, setting standards, governance, and reusable components that empower therapeutic area-aligned teams while driving enterprise-wide consistency, speed, and reliability. You’ll be a technical and strategic leader, guiding cross-functional pods, productizing analytical models, and driving innovation within agentic AI platforms to support forward-looking, actionable brand and omnichannel insights.

Responsibilities:

Tools, Automation, & AI Products

• Product Ownership: Serve as dedicated product owner for the Agentic MMx (Marketing Mix) Platform - own its strategy, vision, roadmap, and stakeholder alignment in partnership with BI&T and Data Science. Drive the ongoing evolution of this self-service analytics hub to deliver advanced capabilities, including KDA, scenario simulation, investment planning, constrained optimization, and real-time decision support.
• Pod Leadership: Lead the Tools & Automated Solutions cross functional team across BI&T, Data Science, TA Analytics, and analytics/engineering to architect, launch, and maintain scalable analytics solutions—covering dashboards, always-on insights, scenario simulators, measurement pipelines, and democratized KDA platforms.
• Collaboration: Partner with BI&T, Data Science, TA Analytics, and engineering to deliver analytics-ready datasets, feature stores, semantic layers, and automated data pipelines, standardizing and accelerating insight generation.
• Efficiency and Innovation: Champion automation, templated workflows, and platformization to reduce manual effort and external vendor reliance, maximizing reuse and operational efficiency. Continuously identify opportunities for tool innovation and scalable enablement of best practice measurement solutions.
• Integrate decision science outputs into annual planning, CRM (e.g., Veeva), and omnichannel orchestration workflows via APIs and embedded dashboards to ensure sustained adoption and business impact.
• Develop monitoring and governance dashboards for real-time oversight of model/data health, user adoption, SLAs, drift/stability, and auditability.

Agentic & AI Capability Development

• Architect and deploy autonomous and semi-autonomous analytics agents using multi-agent frameworks, enabling progression from descriptive analytics to causal analysis, root-cause insights, and predictive recommendations.
• Own the AI product lifecycle: manage proof-of-concept, pilot, rollout, and continuous optimization; establish robust governance covering safety, security, ethical standards, and privacy compliance.

Cross-Functional Team Management & Enablement

• Lead cross-functional pods (BI&T engineers, analytics/data engineers, platform SMEs), managing product roadmaps, agile backlogs, and release cycles.
• Codify and promote engineering standards for deployment, MLOps, CI/CD, QA/testing, SLAs/SLOs, and RACI matrices for reliability and quality.
• Standardize and scale processes, maximize component reuse, minimize vendor dependence, and ensure governance and privacy compliance.
• Coach and enable enterprise and TA-aligned analytics teams, driving best practice adoption, technical enablement, and a culture of consistent delivery and innovation.
• Promote agile ways of working using collaboration platforms (e.g., Jira) for rapid development and communication.

Advance Governance, Compliance & Trust

• Partner with Data Governance, Legal, and Privacy teams to define and enforce data/AI governance: SLAs, RACI, privacy-by-design, and responsible/ethical AI controls.
• Ensure AI tools and solutions are explainable, auditable, and compliant; continuously monitor for bias, document decisions, and incorporate human-in-the-loop mechanisms where required.

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