Associate Principal Scientist, AI for Chemical Toxicology

About the team

Predictive AI and Data team is responsible for providing AI and Bioinformatics solutions to the scientists across the spectrum of drug development and discovery in AstraZeneca (both pre-clinical and clinical stages). The primary aim is to find ways to accelerate the drug development process by leveraging existing company data in combination with the most cutting-edge AI approaches in day-to-day scientific work across the company.

Introduction to role

In this role you will lead the next‑generation predictive safety modelling at scale, applying chemoinformatics, bioinformatics and AI/ML to deliver decision-shaping safety insights and cross-functional scientific leadership. Provide scientific leadership for predictive safety modelling, integrating chemical and biological data (e.g., Safety Omics, Cell Painting, NAMs) to inform risk assessment and progression decisions. This is a fully hands-on, computational role with cross-functional influence.

Accountabilities

·       Develop, optimise and deploy predictive safety models using chemoinformatics, bioinformatics and ML/AI.

·       Create reproducible workflows, data QC procedures and model validation frameworks.

·       Integrate multimodal data: chemical descriptors, omics, imaging and NAM datasets.

·       Serve as scientific lead for project teams and influence program strategy.

·       Drive innovation and identify strategic modelling opportunities across R&D.

·       Lead collaborations with academia, consortia and technology partners.

·       Mentor junior scientists and contribute to capability building.

·       Communicate complex modelling concepts to diverse scientific and governance audiences.

Essential Skills/Experience

·       PhD (or equivalent years of experience) in mathematics, computer science, engineering, physics, statistics, computational sciences, chemoinformatics, bioinformatics, computational toxicology or a related field.

·       Demonstrated experience in implementing machine learning/AI workflows to automate bioinformatics or cheminformatics analysis pipelines, ideally in the Pharma and/or Healthcare space

·       Advanced Python programming skills.

·       Familiarity with GitHub, CI/CD pipeline, and best DevOps and MLOps practices

·       Familiarity with existing machine learning models for chemical structure modelling: GNNs (graph neural networks), directed message-passing neural network (D-MPNN) as well as foundation models (e.g., CheMeleon). Experience with multimodal biological + chemical datasets.

·       Proven leadership in delivering impactful modelling work.

·       Excellent communication and stakeholder engagement skills.

·       Acts as discipline leader and shapes scientific strategy.

·       Solves complex problems using scientific judgement.

·       Builds strong cross-functional relationships and networks.

·       Coaches and mentors others, enhancing team capability.

Desirable Skills/Experience

·       Experience with Safety Omics, Cell Painting or imaging data.

·       Background in toxicology, pharmacology or ADMET.

·       Experience with cloud computing or workflow automation.

·       Track record of publications in top AI conferences or journals in pharmaceutical research (e.g., NeurIPS, ICML, Nature Machine Intelligence, Nature Communications, NEJM AI, etc.)

·       Experience supervising scientists or managing collaborations.

Date Posted

23-9月-2026

Closing Date

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.