Associate Principal Scientist, Biologics AI

About the Role

We are seeking an experienced and visionary Associate Principal Scientist to lead Biologics AI innovation at AstraZeneca’s US R&D centers in Waltham, MA or Gaithersburg, MD. This is a high‑impact scientific leadership role accountable for defining and executing the AI strategy that integrates state‑of‑the‑art machine learning with wet‑lab discovery to accelerate biologics engineering and enable next‑generation biotherapeutics. You will set technical direction, own delivery across multiple programs, and shape data generation at scale—working across computational and experimental functions and with global partners to translate AI into robust, reproducible advances in discovery.

Key Responsibilities

• 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.

Required Qualifications

• 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