Director, Real World Data Strategy

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.

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.
• Serve as a thought leader across the enterprise RWD Strategy team, contributing to shared capabilities, best practices, and strategic frameworks, with particular emphasis on AI integration, that elevate the team's collective impact.

Partner & Vendor Development

• Identify, assess, and engage RWD data providers and external partners to evaluate differentiated partnership opportunities that address emerging or unmet data needs, including AI-powered data and analytics platforms.
• Lead assessment of RWD partners for comprehensive data development capabilities, ensuring partnerships create clear, lasting strategic advantage for BMS.

Qualifications

Education: Advanced degree in health research, life sciences, data science, quantitative sciences, or a related field. PhD preferred but not required.

Experience: 10+ years of relevant experience in real-world healthcare data, data strategy, market intelligence, life sciences, or consulting, with a demonstrated track record of translating complex business and scientific problems into data-driven strategies and outcomes.

• Demonstrated experience applying AI and advanced analytics in an RWD, life sciences, or data strategy context, including familiarity with generative AI, machine learning, or AI-enabled data tools.
• Proven ability to operate as a strategic advisor to senior leaders in a complex, matrixed life sciences organization.
• Deep expertise in RWD sources, data evaluation approaches, research and analytic applications, and the evolving external RWD ecosystem.
• Significant experience engaging with RWD vendors and data partners, including negotiating and shaping strategic relationships.

Skills & Competencies

• AI fluency and innovation mindset: Ability to identify, evaluate, and operationalize AI-driven opportunities that create tangible value for RWD strategy and enterprise customers. Expected to be a visible AI champion within the team.
• Business acumen: Strong ability to frame data and AI opportunities in terms of business value, pipeline impact, and investment return.
• Strategic thinking and executive communication: Ability to synthesize complex data, AI, and market intelligence into concise, compelling recommendations that influence senior decision-making.
• Strong analytical and structured problem-solving skills, with experience building evaluation frameworks and investment cases.
• Familiarity with analytic platforms, federated environments, and AI/data science approaches relevant to RWD access and exploration.
• Knowledge of RWD compliance requirements (HIPAA, GDPR, etc.), including emerging considerations for AI use in healthcare data contexts.
• Exceptional oral and written communication skills across technical and non-technical audiences.
• Collaborative, team-oriented mindset with comfort operating in a fast-changing, cross-functional enterprise environment.