Senior Ai Impact & Applied Innovation Specialist

Astrazeneca; Cambourne; On-site; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Excellent Benefits

Job summary

AstraZeneca plc is seeking a Senior AI Impact and Applied Innovation Specialist to translate frontier AI capabilities into measurable impact across oncology R&D. You will lead cross‑departmental projects, map high‑value AI questions, and steward scientific hypotheses from concept to deployment.

You will drive evaluation standards, harness interdisciplinary expertise, and communicate progress to senior leadership.

Qualifications

• PhD preferred; Master's with substantial relevant experience considered.
• Experience applying AI/ML to clinical, genomic, transcriptomic, or biomedical data in drug development.
• Strong scientific literacy across the oncology drug development pipeline.
• Familiarity with AI in drug development, including foundation models for biological data.
• Ability to translate questions into tractable AI problems with clear success criteria.
• Experience evaluating AI model quality in clinical context, accounting for confounding and generalisation.
• Track record working across multidisciplinary teams with AI/clinical scientists.
• Excellent written and oral scientific communication to non-technical senior audiences.
• Comfort with ambiguity and bias for action; ability to prioritise under uncertainty.

Responsibilities

• AI Landscape and Opportunity Mapping across AZ's capabilities and external data.
• Identify high-priority scientific questions where frontier AI adds value.
• Apply evaluation standards for AI-driven frameworks and R&D goals.
• Design and apply evaluation approaches for generative and agentic systems with benchmarks.
• Advise on automating steps in scientific, clinical and operational research processes.
• Translate hypotheses into time-bounded projects with clear success criteria.
• Establish frameworks to quantify AI project impacts for the business.
• Communicate priorities and status to senior stakeholders across sections.

Skills

AI/ML methods

Biomedical data analysis

Scientific communication

Cross-functional collaboration

Problem framing

Education

PhD in quantitative biology or computational discipline

Master's degree with substantial relevant experience

Job description

Role: Senior AI Impact and Applied Innovation Specialist

Location: Cambridge UK

Salary: Competitive + Excellent Benefits

Are you ready to translate AstraZeneca's frontier AI capabilities into concrete impact for the organisation? The Applied AI Research team, part of the AI Science Innovation (AISI) group within Enterprise AI (EAI), sits at the intersection of frontier AI research and oncology R&D, with a mission to ensure that advances in foundation models, agentic systems, and biological AI find their way to the problems where they will have the greatest impact on how we discover and develop medicines.

As Senior AI Impact and Applied Innovation Specialist, Applied AI Research, you will be involved in both sides of that translation: identifying where frontier AI can address fundamental drug discovery questions for AstraZeneca, and orchestrating focused cross-departmental projects to answer them. You will maintain a structured view of AZ's highest-value AI science problems, mapping which questions most need answers, which frameworks are now mature enough to address them, and where AZ's unique data, biology, and portfolio create irreplaceable scientific advantage. You will also act as the scientific lead and orchestrator for delivering against those problems, assembling the right combination of expertise and resources and holding the scientific hypothesis through to a result that changes a decision.

This is a role for someone familiar with key questions across the drug discovery pipeline, who can attend a frontier AI seminar and immediately see which ideas are applicable to areas from target identification to trial design, and who has the scientific conviction to advocate for a particular problem in the face of competing priorities.

If you thrive at the intersection of machine learning and translational oncology and want to leave a direct mark on the scientific questions that shape AstraZeneca's pipeline, this role is for you.

Typical Accountabilities

• AI Landscape and Opportunity Mapping: Maintain an interactive framework providing a structured, dynamic inventory of AZ's internal and external AI capabilities, including frontier models and platforms.
• Identify high-priority scientific questions, such as those in target discovery, combination therapy, tumor mapping, and trial design, where frontier AI can deliver significant value. Distinguish opportunities unique to AstraZeneca from those addressable with generic tools or public data.
• Scientific Evaluation Standards: Apply scientific expertise and evaluation standards to AI-driven frameworks designed to maintain a live, structured record of R&D goals and decision-making.
• Design and apply evaluation approaches for generative and agentic systems, including harnesses, benchmarks, trajectory-level metrics and validated LLM-as-judge methods, and run structured red-teaming (adversarial testing, failure-mode taxonomies, pre-release and continuous monitoring).
• Advise on and test where AI agents can responsibly automate steps in scientific, clinical and operational research processes, defining oversight points, acceptance criteria and comparison with the manual baseline.
• Forward-Deployed Embedding and Delivery: Translate scientific hypotheses into well-specified, time-bounded projects with pre-defined success criteria, with a clear path to integration and deployment; orchestrate the delivery of these projects.
• Establish frameworks for quantifying impacts of AI projects and measuring impact for the business and implement these for active cross-functional projects.
• Stakeholder Communication and Leadership: Communicate AI science priorities, programme status, and strategic recommendations to senior stakeholders across EAI, Oncology R&D and Biopharma R&D; act as the scientific bridge between AISI strategy and execution-layer teams, ensuring that frontier insight translates into actionable work and that delivery blockers are visible to leadership.

Essential Requirements

• Advanced degree (PhD preferred; Master's with substantial relevant experience considered) in a quantitative biological or computational discipline: computational biology, bioinformatics, machine learning, statistics, or a closely related field.
• Demonstrated experience applying AI/ML methods to clinical, genomic, transcriptomic, or other biomedical data in a drug development or translational research context.
• Strong scientific literacy across the oncology drug development pipeline, from target identification and preclinical biology through biomarker development, clinical trial design, and Phase 1/2 decision-making.
• Familiarity with the current landscape of AI in drug development, including foundation models for biological and clinical data, perturbation prediction, digital pathology, and agentic research systems.
• Demonstrated ability to frame rigorous scientific or clinical questions and translate them into tractable AI problems with well-specified success criteria.
• Experience evaluating the quality of AI models in a scientific or clinical context, including understanding of validation standards, confounding, shortcut learning, and the distinction between prognostic and predictive signals.
• Track record of working effectively across multidisciplinary teams, including both technical AI/ML specialists and experimental or clinical scientists.
• Excellent written and oral scientific communication, including the ability to present technical material to non-technical senior audiences and to distil complex landscapes into actionable recommendations.
• Comfort with ambiguity and a bias for action; able to make well-reasoned prioritisation decisions under uncertainty and to advocate for them.

Desirable Requirements

• Direct experience with generative AI and agentic systems applied to life sciences, particularly for clinical insights.
• Direct experience with multimodal foundation models, particularly for computational pathology, for response prediction, subtyping and other common research tasks.
• Direct experience with multimodal data integration, for example combining clinical, genomic, transcriptomic, imaging, and/or pathology data.
• Familiarity with CRISPR functional genomics, perturbation prediction, or virtual cell approaches and their application to target discovery or combination prioritisation.
• Familiarity with AI benchmarking methodologies and the challenges of evaluating foundation models on clinically meaningful tasks.
• Experience with AI prioritisation or portfolio management methodologies, including problem identification, value assessment, and structured decision frameworks.
• A record of scientific contribution (e.g., peer-reviewed publications), ideally including AI/ML or clinical AI venues, that establishes credibility with technical, clinical, and scientific audiences.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

Date Posted: 28-Oct-2026
Closing Date: 06-Nov-2026

Our mission is to build an inclusive and equitable environment. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.