Senior Scientist, AI for Discovery - New Target Discovery

AstraZeneca plc; Cambridge; On-site; GBP 85,000 - 120,000; Full time

Job summary

AstraZeneca plc is seeking an experienced AI for Discovery specialist to build and deploy agentic AI and predictive modeling tools to accelerate new target discovery across Oncology and BioPharmaceuticals R&D. You will work at the Cambridge site within a cross-functional team, applying AI to real biological problems.

You will develop production-level code and collaborate with scientists and engineers to translate insights into actionable targets and publications.

Qualifications

• PhD or MSc in computational biology, AI/ML, or related quantitative discipline.
• Experience with agentic AI, multi-agent systems, or LLM-based reasoning in scientific problems.
• Experience with AI/ML modeling for biology, including foundation models or graph networks.
• Understanding of drug discovery biology: target identification, validation, mechanisms.
• Strong Python programming for production-quality workflows and pipelines.
• Experience communicating complex findings to varied audiences.

Responsibilities

• Collaborate with therapeutic area scientists to apply agentic AI to target discovery.
• Build multi-agent AI systems integrated into therapeutic area workflows.
• Develop benchmarks for predictive biology models and virtual cell approaches.
• Write production-grade code, pipelines, and data tools for the team and organization.
• Communicate technical results to expert and non-expert audiences.
• Contribute to AstraZeneca’s external visibility through publications and collaborations.

Skills

Python

Agentic AI experience

Biology knowledge

Deep learning

Communication

Curiosity

Education

PhD in computational biology or related field

Bachelor/Master with equivalent research

Tools

PyTorch

JAX

Job description

About AstraZeneca

At AstraZeneca, we put patients first and strive to meet their unmet needs worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you're our kind of person.

About Our Site

The Discovery Centre (DISC) is AstraZeneca's global corporate headquarters and flagship research and development facility, located on the Cambridge Biomedical Campus in Cambridge, UK. The DISC brings together scientists, engineers, laboratory specialists, technology experts, and corporate colleagues in a highly collaborative environment. The facility provides a modern working environment with advanced laboratories and research infrastructure, including robotics, high-throughput screening, computational science, data-enabled research, and AI-supported technologies. Its location on the Cambridge Biomedical Campus provides access to a strong local network of healthcare, academic, and life-sciences expertise, supporting professional development, external collaboration, and scientific exchange across the biomedical sector.

About the Team

The AI for Discovery - New Target Discovery team exists to surface novel drug targets that Oncology and BioPharmaceuticals R&D can act on. We accelerate discovery by building and deploying two core AI capabilities: agentic AI and predictive models of biology to accelerate new target decision making across AZ R&D.

Our team supports both Oncology and BioPharmaceuticals therapeutic areas, sharing learnings across target discovery approaches and connecting new target strategy across AstraZeneca disease areas. We partner closely with therapeutic area scientists in Oncology, CVRM, Respiratory, Immunology, and Infectious Disease - as well as AI engineering and AI research teams.

What the Role Involves

This is a hands-on individual contributor role at the intersection of artificial intelligence and drug discovery biology. You will apply your technical skills in AI and biology to build, deploy, and evaluate agentic AI and predictive modeling tools that support new target discovery across AstraZeneca's therapeutic areas.

Key responsibilities

• Work with therapeutic area scientists to understand their challenges and apply agentic AI and predictive models to accelerate target identification and validation.
• Build and deploy multi-agent AI systems into therapeutic area workflows, translating AI capabilities into actionable scientific insights.
• Develop, evaluate, and benchmark predictive foundation models for in silico biology - including perturbation prediction, virtual cell models, and preclinical efficacy forecasting.
• Write production-quality code, pipelines, and tools that the team and broader organization can rely on.
• Collaborate effectively within cross-functional project teams, communicating technical findings to both expert and non-expert audiences.
• Contribute to AstraZeneca's external visibility through publications, conference presentations, and scientific collaborations.

Essential Skills

• PhD in computational biology, bioinformatics, AI/ML, systems biology, or a related quantitative discipline, or a Bachelor's/Master's degree with equivalent research experience. Candidates with additional post-doctoral or industry experience are equally encouraged to apply.
• Experience with agentic AI, LLM-based reasoning systems, or multi-agent architectures applied to scientific problems.
• Experience with predictive AI modeling for biology - foundation models, graph neural networks, perturbation models, or virtual cell approaches.
• Understanding of drug discovery biology: target identification, target validation, mechanism of action, or translational science.
• Strong technical proficiency in Python (required); experience building production-quality code, pipelines, and tools for scientific applications.
• Hands-on experience with deep learning frameworks (PyTorch, JAX, or equivalent) for model development and evaluation.
• Effective communication of technical and scientific findings to diverse audiences.
• Enthusiasm and persistence in applying AI methods to complex biological problems.

Desirable Skills

• Experience with multi-agent orchestration frameworks or scientific reasoning platforms.
• Knowledge of targeted delivery modalities such as antibody-drug conjugates (ADCs), bispecifics, or degraders.
• Experience integrating multimodal biological data (genomics, transcriptomics, proteomics, functional genomics).
• Familiarity with clinica