Principal Scientist, Biologics AI

About the Role

We are seeking an experienced and visionary Principal Scientist to lead Biologics AI innovation at AstraZenecas US R&D centers in Waltham MA or Gaithersburg MD. This is a highimpact scientific leadership role accountable for defining and executing the AI strategy that integrates stateoftheart machine learning with wetlab discovery to accelerate biologics engineering and enable nextgeneration biotherapeutics. You will set technical direction own delivery across multiple programs and shape data generation at scaleworking 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 wetlab capabilities and deliver measurable impact on pipeline goals.

• Program ownership: Lead multiple crossfunctional discovery initiatives from problem framing through deployment ensuring rapid translation of computational insights into experimental design and decisionmaking.

• Advanced ML innovation: Architect develop and guide application of cuttingedge modelsprotein language models structureinformed and geometric methods de novo/protein design and multimodal learning that fuses sequence structure and biological activity datato solve highvalue scientific problems.

• AIwetlab integration at scale: Establish closedloop designbuildtestlearn 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 highquality data generation curation and metadata; partner with wetlab leaders to design assays and campaigns that maximize ML utility and reproducibility; influence data platform evolution in collaboration with informatics and engineering.

• Endtoend 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 daytoday technical guidance and contribute to recruitment and development of a highperforming team.

• Stakeholder influence and communication: Communicate strategy progress risk and scientific insights to senior stakeholders; influence portfolio decisions and advocate for AIenabled 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 postdegree experience in academia and/or industry; or a Masters 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: Handson leadership in developing and deploying advanced ML (deep learning generative models structureaware and geometric methods sequence/structure multimodal models) for protein sequence modeling structureinformed prediction de novo design or biologics optimization.

• Closedloop integration: Proven success establishing iterative computationalexperimental cycles (e.g. active learning designbuildtestlearn) including designing experiments to interrogate model predictions and improve data/model quality.

• Lifecycle and systems: Experience leading the full ML lifecycle at scaledata design and preprocessing model architecture training/evaluation deployment monitoring and maintenanceusing modern ML frameworks (e.g. PyTorch TensorFlow) and software engineering best practices.

• Data and platforms: Experience with cloudbased 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.

• Crossfunctional leadership: Strong record of influencing and delivering in matrixed multidisciplinary environments bridging AI scientists computational biologists protein engineers and wetlab 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.

Preferred Qualifications

• Protein and antibody engineering: Experience with antibody/nanobody/protein engineering including de novo design and multiobjective 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.

• Multimodal learning: Experience integrating heterogeneous data types (sequence structure biophysics/biochemistry assays highthroughput 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 (peerreviewed publications patents invited talks opensource contributions or community standards).

Why Join Us

As part of AstraZenecas dynamic US biologics R&D community you will play a critical role in shaping the future of AI-driven biologics discovery and engineering. Collaborating across cutting-edge computational and experimental teams youll drive innovation that brings transformative medicines to patients around the world. You will be supported by a collaborative inclusive and empowering environment with unparalleled opportunities for scientific impact and personal growth.

The annual base pay for this position ranges from $186014.40 - $279021.60. Our positions offer eligibility for various incentivesan opportunity to receive short-term incentive bonuses equity-based awards for salaried roles and commissions for sales offered include qualified retirement programs paid time off (i.e. vacation holiday and leaves) as well as health dental and vision coverage in accordance with the terms of the applicable plans.