Senior Principal Scientist / Associate Director, Lead Scientific Data Governance

Role Summary

Senior Principal Scientist / Associate Director, Lead Scientific Data Governance. Lead strategic data governance projects to maximize the value of scientific data assets, ensuring they are structured, interoperable, and ready for advanced analytics and AI applications. Translate complex scientific workflows into scalable, efficient data solutions within a large, matrixed organization to accelerate drug discovery and enable enterprise-wide innovation.

Responsibilities

• Lead the execution of data governance projects across DSc scientific data domains.
• Guide cross-functional teams to define and implement robust data standards, establish metadata requirements, and develop effective stewardship practices.
• Ensure scientific data is captured with rich contextual metadata to enable reuse and interoperability across platforms.
• Facilitate the harmonization of scientific workflows and data products by partnering with matrixed teams across the organization.
• Serve as the business owner of key scientific data applications and work with IT product teams to ensure tailored implementation and support.
• Promote data literacy and drive adoption of data governance frameworks.
• Represent DSc on cross-functional data governance boards and contribute to the enterprise data strategy.

Qualifications

• Advanced degree (Ph.D. or M.Sc.) in life sciences, data science, or a related field.
• 8+ years of experience in pharmaceutical R&D, with proven track record of working with diverse scientific data types (e.g., omics, imaging, screening).
• Proven experience in data management and stewardship, with a focus on enabling harmonization and integration of diverse data assets across multiple systems.
• Track record of successfully leading cross-functional initiatives in complex, matrixed organizations.
• Excellent project management and stakeholder engagement skills.
• Demonstrated ability to translate complex scientific workflows and technical requirements into scalable data solutions.
• Demonstrated ability to communicate data strategy insights and recommendations to cross-functional stakeholders.

Desirable Qualifications

• Experience with database management systems and working knowledge of coding languages commonly used in scientific data workflows (e.g., SQL, Python, R).
• Advanced expertise in FAIR principles, metadata standards, and enterprise data platforms.
• Specialized knowledge of data governance frameworks and data quality metrics within a drug discovery context.