Director of AI and Data Science - Biotechnology

CSL Behring seeks a Director, AI & Data Science to lead strategy and execution of advanced analytics across our biotechnology portfolio. Based in King of Prussia, PA, you will build and guide a high-performing team applying machine learning, real-world evidence, and predictive modeling to accelerate discovery, clinical development, and manufacturing of lifesaving therapies for rare and serious diseases. Partnering with R&D, clinical, and operations leaders, you will champion data governance, model rigor, and ethical AI while translating complex insights into high-impact decisions in a mission-driven, patient-focused culture., * Lead the AI and data science strategy to accelerate discovery, development, and lifecycle management of plasma-derived and recombinant therapies

• Build and mentor a high-performing AI and data science team, fostering a culture of innovation, scientific rigor, and ethical data use
• Partner with R&D, clinical, manufacturing, and commercial teams to design and deliver AI solutions that improve patient outcomes and operational efficiency
• Oversee development and deployment of machine learning, predictive modeling, and advanced analytics across large-scale clinical, real-world, and operational data
• Establish best practices for data governance, model validation, and regulatory-aligned documentation in a Gx
• P-aware environment
• Evaluate and integrate new AI technologies and platforms; drive build vs. buy decisions and manage external vendors or collaborations
• Translate complex analytical insights into clear recommendations for senior leadership and functional stakeholders
• Define and track KPIs to measure impact of AI initiatives on pipeline productivity, quality, and time-to-market
• Ensure compliance with privacy, security, and ethical standards across all AI and data science initiatives
• Support talent development, succession planning, and cross-functional data literacy across the organization

• Machine learning
• Deep learning
• Statistical modeling
• Python
• RSQLCloud platforms (AWS/Azure/GCP)
• MLOps and model deployment
• Data engineering and data pipelines
• Real-world evidence analytics
• Clinical and biomedical data analysis
• Data governance and privacy
• AI product management
• People leadership and team building
• Stakeholder management