Director, Biologics AI

AstraZeneca seeks an Associate Principal Scientist to lead artificial intelligence strategy for biologics discovery and engineering across US R&D sites. The role combines technical machine learning expertise with cross-functional leadership to integrate computational methods into experimental workflows and advance biotherapeutic pipeline goals.

Responsibilities

• Define and execute AI strategy for biologics discovery, setting roadmaps that integrate computational and experimental capabilities
• Lead multiple cross-functional programs from problem definition through deployment, translating computational insights into experimental design
• Architect and guide application of advanced machine learning models including protein language models, structure-informed methods, and de novo design
• Establish and formalize closed-loop workflows with experimental teams, including feedback cycles and active learning to improve model reliability
• Mentor and develop scientists across AI/ML and experimental domains; contribute to team recruitment and capability building

Requirements

• PhD in computer science, machine learning, bioinformatics, computational biology, or related quantitative field with 8+ years post-degree experience; or Master's with 12+ years
• Demonstrated track record applying AI/ML to proteins, antibodies, or biologics with examples of experimental translation or pipeline impact
• Hands-on expertise developing and deploying advanced machine learning, including deep learning, generative models, and structure-informed methods for protein design
• Proven success establishing iterative design-build-test-learn cycles with closed-loop computational-experimental integration
• Full ML lifecycle leadership experience: data design, model development, training, deployment, monitoring using modern frameworks like PyTorch or TensorFlow
• Experience with cloud-based ML environments, scalable data workflows, and ability to partner with data engineering teams
• Strong cross-functional leadership in matrixed environments bridging AI, computational biology, protein engineering, and wet-lab teams