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Salary
$40k – $97k per year (Estimated)
Location
In office (Beijing)
Seniority
Principal
Employment
Full-Time
Overview
Company
Impact
Profile match
AstraZeneca is a British-Swedish pharmaceutical company formed in 1999 by the merger of Astra of Sweden and Zeneca of the United Kingdom, and now one of the largest drug makers in the world by revenue. Its portfolio is concentrated in oncology with Tagrisso, Imfinzi and Enhertu, alongside cardiovascular, renal and metabolic products including Farxiga, respiratory and immunology medicines, and a rare disease business built on the acquisition of Alexion. Headquartered in Cambridge in England and listed in London, Stockholm and New York, it reports in United States dollars.

About the team

PredictiveAI and Data teamis responsible forproviding 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 byleveragingexisting company data in combination with the mostcutting-edgeAI approaches in day-to-day scientific work across the company.

Introduction to role

In this role you will leadthenext-generation predictive safety modelling at scale, applyingchemoinformatics, 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 fullyhands-on,computational role with cross-functional influence.

Accountabilities

  • Develop,optimiseand deploy predictive safety models usingchemoinformatics,bioinformaticsand ML/DL.

  • Create reproducible workflows, data QCproceduresand model validation frameworks.

  • Integrate multimodal data: chemical descriptors, omics,imagingand NAM datasets.

  • Serve asscientificlead for project teams and influence program strategy.

  • Drive innovation andidentifystrategic modelling opportunities across R&D.

  • Lead collaborations with academia,consortiaand technology partners.

  • Mentor junior scientists and contribute to capability building.

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

Essential Skills/Experience

  • PhD inChemoinformatics, Bioinformatics, ComputationalToxicologyor relatedfield.

  • StrongAI/ML experience (PyTorch, TensorFlow, scikit-learn).

  • Advanced Python or R programming skills.

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

  • 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.

  • Solvescomplex problems using scientific judgement.

  • Builds strong cross-functional relationships and networks.

  • Coaches andmentorsothers, enhancing team capability.

Desirable Skills/Experience

  • Experience with Safety Omics, CellPaintingor imaging data.

  • Background in toxicology,pharmacologyor ADME.

  • Experience with cloud computing or workflow automation.

  • Track recordof 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

17-8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.

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