AstraZeneca
Drug Development
Director,AIResearch
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“Director, AI Research at AstraZeneca. Skills: AI Research, Machine Learning, ML Ops, Research Strategy. Lead roadmap for priority problem spaces. Develop high-value AI capabilities”
What You'll Achieve.
Accelerate drug development; Improve patient outcomes; Improve decision quality; Automate key discovery steps
Industry & Context.
What They're Looking For.
Must Have
PhD in computer science, statistics, applied mathematics, or a related area (or an MSc with 5 years of relevant background), Minimum of 2 years developing machine learning models in an industry setting, Machine Perception expertise, Explainability techniques expertise, Experience applying AI to pathology imaging, radiological analysis, diagnostics, prognostics, or clinical Quality Control, Practical experience with model tracking, Practical experience with model governance, Practical experience managing multiple models in diverse production contexts, Technical Leadership ability
Nice to Have
Research programme demonstrated by prestigious publications (e. g. , Nature Machine Intelligence, NeurIPS, ICML) with at least one as lead author, Deep understanding of the drug development or clinical trial processes, Track record of successfully working with AI engineering teams to deploy complex predictive algorithms
What You'll Do.
Lead roadmap for priority problem spaces
Develop high-value AI capabilities
Invent and apply methods in deep learning
Invent and apply methods in representation learning
Invent and apply methods in reinforcement learning
Invent and apply methods in active learning
Tailor metrics to domain-specific challenges
Convert scientific advances into production-grade models
Establish robust practices for model tracking
Establish robust practices for model governance
Establish robust practices for model lifecycle management
Advise and co-create with partners
Translate business needs into ML solutions
Publish in top-tier venues
Represent AstraZeneca at conferences
How You'll Work.
Team & Collaboration
Co-create with partners across therapeutic areas; Work with AI engineering teams
Communication Scope
Multidirectional communication
Full Job Description
## **Introduction to the Role** This position focuses on guiding AI research that converts complex, multimodal data into decisions to accelerate drug development and improve patient outcomes. It is a fantastic opportunity to thrive in an environment where bold ideas evolve into deployed models and peer-reviewed science, working alongside leading scientists, engineers, and product leaders! As the Director of EAI, AI Research, the objective is to lead a high-calibre, interdisciplinary team advancing machine learning across multiple therapeutic areas. The role entails setting the research agenda, inventing new methods, and transforming prototypes into robust, governed solutions that support imaging, diagnostics, and clinical validation pipelines. This offers a unique chance to build strategy from the ground up while staying hands-on with cutting-edge methodologies. ## **Accountabilities** * **Research Strategy** : Lead the roadmap for priority problem spaces, supporting teams to develop high-value AI capabilities using modern engineering standards. * **Multimodal ML Innovation** : Invent and apply methods in deep, representation, reinforcement, and active learning, tailoring metrics to domain-specific challenges. * **Translational Pipeline Impact** : Convert scientific advances into production-grade models that improve decision quality and automate key discovery steps. * **ML Ops and Governance** : Establish robust practices for model tracking, governance, and lifecycle management, ensuring responsible AI use. * **Cross-Functional Partnership** : Advise and co-create with partners across therapeutic areas, translating business needs into effective ML solutions. * **Scientific Contribution** : Mentor researchers, publish in top-tier venues, and represent AstraZeneca at leading conferences! * **Stakeholder Alignment** : Maintain clear, multidirectional communication across the organisation regarding goals, risks, and results. ## **Essential Skills and Experience** * **Ac
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