Associate Principal Scientist, Biologics AI

• Strategic leadership and vision: Define and drive the AI strategy for biologics discovery and engineering, setting priorities and roadmaps that integrate AI and wet‑lab capabilities and deliver measurable impact on pipeline goals.
• Program ownership: Lead multiple cross‑functional discovery initiatives from problem framing through deployment, ensuring rapid translation of computational insights into experimental design and decision‑making.
• Advanced ML innovation: Architect, develop, and guide application of cutting‑edge models—protein language models, structure‑informed and geometric methods, de novo/protein design, and multi‑modal learning that fuses sequence, structure, and biological activity data—to solve high‑value scientific problems.
• AI–wet‑lab integration at scale: Establish closed‑loop design–build–test–learn workflows with experimental teams, formalizing feedback cycles, uncertainty quantification, and active learning to improve model reliability and throughput.
• Data strategy and governance: Set standards for high‑quality data generation, curation, and metadata; partner with wet‑lab leaders to design assays and campaigns that maximize ML utility and reproducibility; influence data platform evolution in collaboration with informatics and engineering.
• End‑to‑end ML lifecycle leadership: Oversee and improve processes across data pipelines, model development, validation, deployment, monitoring, and continuous improvement, including best practices for reproducibility, documentation, and scientific rigor.
• Technical mentorship and team development: Mentor and upskill scientists across AI/ML and experimental domains; provide day‑to‑day technical guidance and contribute to recruitment and development of a high‑performing team.
• Stakeholder influence and communication: Communicate strategy, progress, risk, and scientific insights to senior stakeholders; influence portfolio decisions and advocate for AI‑enabled approaches internally and with external partners.
• External scientific leadership: Drive publications, patents, and external visibility; represent AstraZeneca in collaborations and at scientific venues; evaluate and integrate emerging methods and tools.

• Education and experience: PhD in computer science, machine learning, bioinformatics, computational biology, physics, chemistry, mathematics, engineering, or a related quantitative field, with typically 8+ years of relevant post‑degree experience in academia and/or industry; or a Master’s with 12+ years of relevant experience.
• Domain impact in biologics AI: Demonstrated track record applying AI/ML to proteins, antibodies, or related biologics, with clear examples of methods translated into experimental outcomes, platform capabilities, or pipeline decisions.
• Deep technical expertise: Hands‑on leadership in developing and deploying advanced ML (deep learning, generative models, structure‑aware and geometric methods, sequence/structure multi‑modal models) for protein sequence modeling, structure‑informed prediction, de novo design, or biologics optimization.
• Closed‑loop integration: Proven success establishing iterative computational–experimental cycles (e.g., active learning, design–build–test–learn), including designing experiments to interrogate model predictions and improve data/model quality.
• Lifecycle and systems: Experience leading the full ML lifecycle at scale—data design and preprocessing, model architecture, training/evaluation, deployment, monitoring, and maintenance—using modern ML frameworks (e.g., PyTorch, TensorFlow) and software engineering best practices.
• Data and platforms: Experience with cloud‑based ML environments and scalable data workflows; ability to specify requirements and partner with data engineering/IT to evolve production ML systems that support discovery at scale.
• Cross‑functional leadership: Strong record of influencing and delivering in matrixed, multidisciplinary environments, bridging AI scientists, computational biologists, protein engineers, and wet‑lab teams across sites.
• Scientific communication: Excellent communication skills with the ability to synthesize complex technical concepts for diverse audiences and to shape scientific and portfolio decisions.
• Innovation and delivery: Evidence of scientific innovation and impact through publications, patents, platform creation, or deployment of AI methods that materially improved experimental or business outcomes.

• Protein and antibody engineering: Experience with antibody/nanobody/protein engineering, including de novo design and multi‑objective optimization for developability, stability, and functional performance.
• Advanced methodologies: Expertise with generative models (e.g., diffusion, autoregressive LMs), geometric deep learning/graph neural networks, Bayesian optimization, uncertainty quantification, and active learning for guided experimentation.
• Multi‑modal learning: Experience integrating heterogeneous data types (sequence, structure, biophysics/biochemistry assays, high‑throughput binding/functional data, bioprocess/developability metrics) into unified models.
• Productionization and MLOps: Experience leading deployment of scientific software/ML models into production discovery workflows, including model monitoring, versioning, and compliance with governance standards.
• Data generation strategy: Demonstrated ability to design or refine assay strategies and experimental campaigns to maximize downstream ML performance and data reuse, including metadata standards and FAIR principles.
• People and project leadership: Prior experience leading scientists and managing complex projects or collaborations; ability to set goals, delegate effectively, and deliver against timelines.
• External profile: Strong external scientific presence (peer‑reviewed publications, patents, invited talks, open‑source contributions, or community standards). 

Date Posted

Closing Date

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