Head of Data, AI & Automation (all genders)

1. Automation as a Value Engine (Primary Focus)

Drive automation across:

• Discovery & Preclinical Services
• R&D / Innovation
• Drug Development
• Enterprise Platforms (e.g. ERP, Finance, HR)
• Identify and scale quick-win automation use cases (e.g. RPA in ERP, workflow automation)
• Establish automation as a standard lever for efficiency and quality improvement
• Define and track automation-driven value (cost, speed, quality)

1. Data & AI Platform Leadership

• Build and operate a scalable enterprise data platform
• Enable: Data integration across lab, manufacturing and enterprise systems and Self-service analytics and data access
• Provide the foundation for AI and advanced analytics at scale

1. AI Enablement & Scaling

• Move from isolated AI use cases to industrialized AI deployment
• Define and govern the AI lifecycle (use case * development * scaling)
• Support: Scientific AI (In Silico, analytics), Operational AI (process optimization, automation)

1. Domain Enablement (Business Alignment)

Work closely with:

• Discovery & Preclinical Services
• R&D / Innovation
• Manufacturing
• Enterprise Functions
• Ensure automation and data solutions are:
• Business-driven
• Embedded in workflows
• Scalable across domains

1. Governance & Standards

Define standards for:

• Data architecture
• Automation tools and platforms
• AI development and deployment
• Ensure alignment with:
• Data governance (business-owned)
• Security & compliance (InfoSec-defined)

Master’s degree in Data Science, Artificial Intelligence, Business Informatics, or a related field.

• Strong experience in Data platforms and architecture, Automation technologies (RPA, workflow, low-code), AI / advanced analytics
• Proven track record in Delivering business value (not just technology), Scaling capabilities across complex organizations
• Ability to: Bridge business and technology, drive change and adoption and operate in regulated environments (GxP is a plus)