Director, AI&Data Science

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.

Responsibilities

• 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 Required Skills

• 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