
Data Scientist
- Ireland
- Permanent
- Full-time
- Develop the next generation of AI-driven digital diagnostic technologies to support AstraZeneca’s 2030 ambition and increase access to our medicines.
- Optimize the quality and depth of insight from clinical biomarker data, supporting faster data-driven decisions and empowering future strategy and planning for AZ’s assets.
- Unlock the full potential of both internal and real-world biomarker data sources by developing or leveraging existing tools and analytics for deep exploration.
- Advanced degree (M.S./Ph.D.) in Computer Science, Statistics, Mathematics, Data Science, or a related field.
- At least 3-5 years proven industry experience delivering data science and ML/AI solutions in production at scale.
- Proficiency in statistics, machine learning, deep learning, with multi-modal data in a variety of applications.
- Fluent in AI tech full stack: Git, Python, Pytorch, Docker, experiment tracking (e.g., MLFlow, Amazon Sagemaker, etc.), and CI/CD-driven MLOps.
- Proven ownership of the full AI lifecycle- from data acquisition and labeling through model development, validation, deployment, post-deployment monitoring, and continual retraining.
- Hands-on with experimental design, statistical analysis, and rapid prototyping.
- Track record of close collaboration with software-engineering, DevOps, product, and domain-expert teams to translate AI research into reliable, maintainable production code.
- Knowledge of cloud platforms such as AWS, Google Cloud, or Azure, and on-prem and on-the-edge.
- Strong analytical, critical-thinking, project management, and organizational skills.
- Ability to communicate complex technical concepts to technical and non-technical audiences.
- Experience in AI healthcare: digital pathology, medical imaging, genomics, or diagnostics.
- Working knowledge of SaMD/IVD regulations and quality standards (ISO 13485, IEC 62304, GCP, ICH).
- Peer-reviewed publications in leading AI / data-science conferences and journals (e.g., NeurIPS, CVPR, MICCAI, Nature Medicine) demonstrating methodological or clinical impact.
- Established professional networks in data science, software engineering, or diagnostics communities.