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. Translate complex scientific workflows into scalable data solutions within a matrixed organization to accelerate drug discovery and 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

• Essential: Advanced degree (Ph.D. or M.Sc.) in life sciences, data science, or a related field.
• Essential: 8+ years of experience in pharmaceutical R&D, with diverse scientific data types (e.g., omics, imaging, screening).
• Essential: Proven experience in data management and stewardship, enabling harmonization and integration of diverse data assets across multiple systems.
• Essential: Track record of leading cross-functional initiatives in complex, matrixed organizations.
• Essential: Excellent project management and stakeholder engagement skills.
• Essential: Demonstrated ability to translate complex scientific workflows and technical requirements into scalable data solutions.
• Essential: Demonstrated ability to communicate data strategy insights and recommendations to cross-functional stakeholders.
• Desirable: Experience with database management systems and coding languages (SQL, Python, R).
• Desirable: Advanced expertise in FAIR principles, metadata standards, and enterprise data platforms.
• Desirable: Specialized knowledge of data governance frameworks and data quality metrics in a drug discovery context.

Skills

• Data governance
• Data management and stewardship
• Metadata and data standards
• Cross-functional collaboration
• Strategic data literacy and adoption
• Communication of data strategy

Education

• Advanced degree (Ph.D. or M.Sc.) in life sciences, data science, or related field