Senior Manager, Real World Data Strategy

About the role

The Real-World Data (RWD) Strategy team stewards real-world data assets end-to-end, from strategic acquisition and governance to scalable data product development and AI-ready foundations, to democratize access, accelerate innovation, and improve patient outcomes. The Senior Manager is a dynamic, cross-functional role spanning data landscaping, external partnerships, customer success, data product strategy, and governance. This individual translates strategic priorities into structured execution plans within a matrixed, collaborative environment, championing an AI-native mindset throughout.

Responsibilities

• Lead end-to-end RWD initiative execution, from scoping through stakeholder sign-off, with clear milestones, owners, and risk mitigation strategies
• Track interdependent workstreams, proactively surface blockers, and escalate with solutions
• Act as the team's AI driver: identify, pilot, and embed AI/ML tools and automation into RWD workflows, data pipelines, and analytical processes
• Ensure RWD assets are structured and documented for AI/ML downstream consumption in partnership with engineering and data science teams
• Evaluate and onboard external RWD sources (claims, EHR, registries, labs, PROs); manage vendor assessments, acquisition workflows, and data access governance
• Design scalable data products across Research, Development & HEOR; engage internal customers to drive adoption and usability
• Define and track KPIs demonstrating the impact of RWD investments and data products
• Uphold data quality, governance standards, metadata practices, and compliance requirements; contribute to lineage documentation and enablement resources

Requirements

Education: Bachelor's in Life Sciences, Data/Analytics, Epidemiology, Public Health, or related field (required); Master's degree (MPH, MBA, Biostatistics, or Data Science) (preferred)

Experience: 4–5 years in data, analytics, real-world evidence, consulting, or a related field; Hands-on experience with RWD sources (claims, EHR, molecular, biomarker, imaging), including knowledge of their structure, limitations, and appropriate use cases; Track record delivering data products or analytics solutions scoping, timeline management, and fit-for-purpose outputs (e.g., analytical datasets, data platforms, self-service tools); Experience in cross-functional, matrixed environments across technology, analytics, and business stakeholders; Demonstrated hands-on proficiency with AI-native tools, specifically Claude Code and collaborative AI coding environments, applied to data workflows, scripting, or pipeline development; a pre-interview hands-on project may be required

Skills

Technical: SQL proficiency; working knowledge of R or Python for data manipulation, QC, or analytical workflows; Familiarity with data pipelines and cloud environments (e.g., Databricks, AWS); Proficiency with Claude Code and collaborative AI tools to accelerate querying, QC automation, and documentation; ability to integrate AI into structured workflows via prompt engineering and AI-assisted analysis; Understanding of data quality frameworks and governance practices

Professional & Leadership: Strong execution and delivery management – organized, detail‑oriented, able to juggle competing priorities; Hands‑on AI capability is a core, non‑negotiable requirement – candidates must demonstrate active, daily use of AI tools (including Claude Code) with tangible examples of impact on workflows, automation, and team delivery; this is an expected competency aligned with BMS's enterprise‑wide AI‑native commitment; Clear communicator across technical and non‑technical audiences; Collaborative, solutions‑oriented; builds trust across stakeholder groups; Comfortable with ambiguity and able to flex across functional areas as the role evolves