The Position:
The R&D Data Excellence Lead drives the vision, strategy, and execution of data excellence across the R&D organization. This role ensures that data is standardized, governed, and leveraged for advanced insights across scientific, clinical, safety, and regulatory domains. The role serves as a strategic advisor to R&D leadership, overseeing enterprise-aligned data infrastructure, data architecture, and standards to accelerate discovery, development, and decision-making. The role partners closely with enterprise I&T to ensure all data initiatives are scalable, compliant, and interoperable.
Responsibilities:
Define and execute the R&D-wide data and analytics strategy aligned to R&D 2.0 transformation goals and PD&C objectives
Serve as a strategic advisor to R&D leadership on opportunities to improve pipeline productivity and operational efficiency using data
Establish and govern data standards, metadata management, lineage, and ontologies across R&D domains
Ensure compliance with enterprise data governance and facilitate its application across R&D data sets
Partner with I&T to design and maintain scalable, secure, and interoperable data platforms
Enable integration of diverse data types including clinical, real-world data, omics, imaging, and digital biomarkers
Collaborate with Digital Business Partners and Analytics teams to ensure data readiness and accessibility for advanced analytics
Foster cross-functional data alignment across clinical, safety, regulatory, and research domains
Lead and develop a high-performing team of data stewards, scientists, and architects
Engage external partners, consortia, and regulatory bodies to influence and align with industry data innovation
Education & Requirements:
Advanced degree (MS, PhD, or equivalent) in Data Science, Bioinformatics, Statistics, Computer Science, or a related field.
10+ years of data strategy and analytics experience in life sciences industry, with 5+ years in a leadership capacity.
Strong knowledge of R&D processes, including discovery, translational, clinical development, and regulatory submissions
Proven experience in enterprise data governance, architecture, and platform design in GxP environments.
Familiarity with AI/ML enablement, though not focused on algorithm development.
Strong stakeholder engagement, communication, and change leadership skills.
Expertise with modern data stack: cloud platforms, data lakes, FAIR principles, and standards like CDISC, HL7/FHIR
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