AI Scientist

Typical Accountabilities

Major responsibilities 
•    Apply and adapt large-scale foundation models (such as transformer-based architectures and pretrained multi-modal models) for biomedical research, including knowledge extraction, prediction tasks, and generative modelling.
•    Design, finetune, and validate machine learning algorithms—including foundation models—tailored to biological, multi-omics, and clinical datasets relevant to drug development. 
•    Create and implement novel computational algorithms to address challenges in disease biology, biomarker identification, patient stratification, and therapeutic response prediction.
•    Collaborate closely with bioinformatics experts and therapeutic area scientists to harmonize and integrate diverse multi-modal datasets—including genomics, proteomics, clinical, and imaging data—and apply advanced AI-driven methodologies to enable impactful translational research initiatives.
•    Present and explain analytic findings and model outputs to cross-functional teams, translating complex results into actionable recommendations.
•    Act as an AI advocate across multidisciplinary teams; promote understanding, adoption, and best practices for AI and machine learning methods in drug research, and mentor colleagues on leveraging advanced analytics for scientific discovery.
•    Champion reproducible research, code documentation, and ethical data science practices in sensitive research environments.

Education, Qualifications, Skills and Experience
•    PhD or MSc in Data Science, Computer Science, Biomedical Engineering, Bioinformatics, Applied Mathematics, or related field.
•    Hands-on experience developing, adapting, and finetuning foundation models (such as transformer architectures, large language models, and pretrained multi-modal models) for biomedical or healthcare applications.
•    Proficiency in model building, machine learning (including deep learning), algorithm development, and large-scale data processing in Python, R, or equivalent languages.
•    Minimum of 5 years of hands-on experience in data science, bioinformatics, or a related field, with a strong track record of delivering impactful results in a pharmaceutical or healthcare context.
•    Experience of addressing data compliance & localisation challenges with China data compliance right (e.g., CBDT, CSL) in the data platform related project is a strong plus.  
•    Agile experience is a plus.

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 authorisation and employment eligibility verification requirements.