Director of AI Research, AI for Oncology Clinical Development

Location: Barcelona, Spain or Cambridge, USA

Title: Director

We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors: you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real.

Drug discovery has benefitted enormously in the current AI era, yet comprises only a portion of the journey to bring new treatments to those in need. The final step – clinical drug development – is oft overlooked, despite requiring a significant proportion of time and investment. In the AI for Clinical Development team at AstraZeneca, we're reimagining the process of clinical development. Our vision is to bring safe, efficacious treatments to patients in a way that quantifiably improves our chance to do this faster, more cost-effectively, and with reduced patient burden.

In this role, you will be a senior technical and strategic lead to help us leverage the power of AI to the fullest, alongside our other computational, statistical, and machine learning tools. You will work across the enterprise to define and deliver on AstraZeneca’s most pressing clinical development questions. You will proactively collaborate in cross-functional teams spanning AstraZeneca’s key Oncology foci of hematology, cell therapy, antibody-drug conjugates, small molecules, and biologics. This is an unprecedented, high visibility opportunity to invent new ways to leverage data, models, and learnings across the spectrum of cancer biology and drug modalities – and importantly, you and the team will apply these new methods to measurably advance the late-stage drug pipeline and our group’s ambition.

Responsibilities

Define and drive the AI strategy and roadmap for Oncology early and late phase clinical development, and align AI/ML priorities with clinical and business objectives

Lead, by matrix influence and scientific authority, delivery of complex, high-stakes AI projects

Evaluate, develop, and champion cutting-edge AI methods, end-to-end, including problem definition, data considerations, governance, algorithm development, validation, and adoption

Build cross-functional relationships with clinical development, biometrics, regulatory, and study teams to embed AI strategy and validated solutions into clinical study design, execution, strategy, and decision-making

Establish and maintain external collaborations with academic institutions, technology partners, and industry consortia to access novel capabilities and advance the AI roadmap

Represent AstraZeneca at scientific conferences, standards bodies, and peer-reviewed venues; contribute first- or last-author publications in leading ML and clinical AI journals

Establish best practices; help shape and promote team culture

Mentor and support more junior level scientists within the team

Required qualifications

PhD in a quantitative discipline such as computer science, mathematics, physics, biophysics, bioinformatics, computational biology, computational neuroscience, biostatistics

4-8 years’ work experience outside of PhD with measurable impact (e.g. models delivered, patents, SaMD filings, first-author publications, open-source projects, standards-body participation)

Technical requirements

Exceptional software development and coding skills, leveraging frontier coding agent frameworks; knowledge of computing hardware a plus

Deep understanding of machine learning fundamentals, with domain expertise in one or more of the following;

o Training and tuning foundation models

o Bayesian inference

o Temporal modeling

o Multimodal integration and modeling

o Model calibration and domain adaptation

o Data-centric AI: acquiring, creating, and curating datasets for model training / post-training / benchmarking / evals

o Model and data evaluations and benchmarking

o Model interpretability

o Model post-training and alignment

o Multi-agent systems and agent harnesses

Preferred skills

Deep, up-to-date knowledge of ML literature and connections with the ML community (e.g. conference publications, presentations, or collaborations)

Experience, knowledge, and understanding of one or more fields of biology

Experience working with biological data such as molecular (e.g. DNA, RNA, protein), imaging (radiology, microscopy), or clinical text (e.g. EHR, clinical notes)

Experience in drug development including but not limited to clinical trial design, biomarker discovery, companion diagnostics, dosing, safety, endpoints, and regulatory

Experience in a matrixed global organization spanning multiple sites and therapy areas

Soft skills

Strong proficiency in augmenting but not supplanting daily knowledge work with agentic tools

Team-oriented mindset

Ability to proactively and independently deliver high-quality contributions at pace

Up-to-date with the latest AI research and tools, proactively trying out those of interest, and ability to discern hype from true added value

Comfort with ambiguity and a mindset to learn in public, prototype early, and fail forward

Excellent written and verbal communication skills

In-Office Working Requirements

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.

The annual base pay for this position ranges from $ 186.014,40 to $279.021,60. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.

Ready to make a difference? Apply now!

Why AstraZeneca:

Here, science and data come together to solve urgent health challenges with focus and pace. You will collaborate with diverse experts who challenge assumptions, share perspectives, and move quickly from whiteboard to real-world impact for patients. We invest in cutting-edge technology, empower people to take smart risks, and pair high ambition with genuine support—valuing kindness alongside bold thinking. Your work will shape how evidence is generated, trusted, and scaled across medicines that matter worldwide.

Call to Action:

Step into a role where your evidence strategy will shape portfolio bets, strengthen regulatory confidence, and open access for patients—make your next move count.

#EAI

Date Posted

28-okt.-2026

Closing Date

11-nov.-2026 Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.