Director, Real World Data Strategy

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.

Position Summary:

At BMS, the Director, Real-World Data Strategy sits at the intersection of business strategy, data intelligence, and AI-driven innovation, serving as a key member of the enterprise Real-World Data (RWD) Strategy team. This is a business-first role powered by data and accelerated by artificial intelligence. The Director is responsible for translating BMS's most pressing business and R&D priorities into sharp, actionable RWD strategies, and for leading the team's adoption and application of AI to reshape how the organization identifies, evaluates, acquires, and deploys real-world data assets to drive evidence generation, competitive differentiation, and patient impact.

This role is expected to be the AI innovation driver on the enterprise RWD Strategy team, someone who brings a high AI IQ, actively identifies where AI unlocks new strategic value, and sets the standard for how AI thinking is embedded in the team's work and in service of BMS customers and partners.

As part of an enterprise-wide team, this Director will serve as a connector between the external RWD and AI landscape and BMS's internal needs, ensuring that every data and technology investment is grounded in clear business value and strategic alignment. Success in this role requires deep domain expertise, AI fluency and curiosity, executive presence, rigorous analytical thinking, and a relentless focus on outcomes for the business.

Key Responsibilities:

AI Leadership & Innovation:

• Serve as the team's AI innovation lead, setting direction for how artificial intelligence and advanced analytics are integrated into RWD strategy, data evaluation, market intelligence, and evidence generation.
• Proactively identify, evaluate, and champion AI-enabled data opportunities and emerging technologies that enhance RWD exploration, dataset assessment, and strategic decision-making at scale.
• Translate the rapidly evolving AI and data science landscape into practical, high-impact applications aligned with BMS R&D priorities and pipeline needs.
• Model AI-forward thinking across the team and for customers, building shared literacy, frameworks, and best practices that elevate the team's collective AI capability.
• Align novel AI-driven research ideas with available RWD assets to accelerate exploratory use cases, enabling faster, smarter data-driven decisions across the enterprise.

Business-Driven Strategy & Prioritization:

• Champion and communicate a forward-looking RWD exploration strategy that is anchored in BMS business priorities, translating R&D needs, pipeline goals, and evidence gaps into targeted data investment and exploration decisions.
• Synthesize inputs from RWD users, senior leaders, and cross-functional partners to define and evolve strategic priorities, ensuring every data initiative is tied to measurable business value.
• Develop and present clear, executive-ready business cases for RWD investments, acquisitions, and licensing decisions, balancing strategic ambition with budget realities and portfolio needs.

Data Landscaping & Opportunity Identification:

• Monitor and interpret the global RWD ecosystem, including emerging datasets, vendors, technologies, AI platforms, partnerships, and market trends, through a business lens, connecting evolving research questions and R&D needs with differentiated data sources and novel use cases.
• Lead rigorous evaluation and piloting of priority RWD sources, assessing fitness, quality, coverage, differentiation, and applicability, and translating findings into clear investment or adoption recommendations for senior stakeholders.
• Establish scalable assessment frameworks, dashboards, and methodologies that enable consistent, repeatable evaluation and prioritization of RWD opportunities across the enterprise portfolio.

Stakeholder Engagement & Enterprise Collaboration:

• Drive high-visibility engagement with senior leaders and cross-functional teams through a hub model of ongoing consultations, ensuring transparent, interactive strategy development processes that build trust and alignment.
• Collaborate across Strategy, Acquisition, Data Product, Governance, Customer Success, and R&D stakeholder groups to convert exploration insights into actionable data, investment, partnership, and portfolio decisions.

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