Director of AI Research, AI for Oncology Clinical Development

AstraZeneca•Cambridge, MA

14d•$186,014 - $279,022•Hybrid

About The Position

AstraZeneca is building a connected, end-to-end Enterprise AI engine that brings together data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across its value chain. This role calls for a collaborative leader who can connect existing capabilities, promote reuse, and scale impactful ideas across geographies and functions. Within the AI for Clinical Development team, the Director of AI Research will help reimagine clinical drug development by applying AI alongside computational, statistical, and machine learning tools. The role serves as a senior technical and strategic lead, working across the enterprise to address pressing clinical development questions and collaborating with teams focused on oncology areas including hematology, cell therapy, antibody-drug conjugates, small molecules, and biologics. The work aims to help bring safe and effective treatments to patients faster, more cost-effectively, and with reduced patient burden, while measurably advancing the late-stage drug pipeline. The position is based in Barcelona, Spain, or Cambridge, USA, and follows an in-office expectation of an average minimum of three days per week, with flexibility. The annual base pay range is $186,014.40 to $279,021.60. The role is eligible for incentives, including short-term incentive bonuses and equity-based awards for salaried roles.

Requirements

PhD in a quantitative discipline such as computer science, mathematics, physics, biophysics, bioinformatics, computational biology, computational neuroscience, or biostatistics.
Four to eight years of work experience outside of the PhD, with measurable impact such as models delivered, patents, SaMD filings, first-author publications, open-source projects, or standards-body participation.
Exceptional software development and coding skills, including use of frontier coding agent frameworks; knowledge of computing hardware is a plus.
Deep understanding of machine learning fundamentals, with domain expertise in one or more relevant areas, including foundation models, Bayesian inference, temporal modeling, multimodal modeling, model calibration and domain adaptation, data-centric AI, model and data evaluation and benchmarking, interpretability, model post-training and alignment, or multi-agent systems and agent harnesses.
Strong proficiency in using agentic tools to augment, rather than supplant, daily knowledge work.
Team-oriented mindset and ability to deliver high-quality contributions proactively, independently, and at pace.
Up-to-date knowledge of AI research and tools, willingness to try relevant tools, and ability to distinguish hype from genuine added value.
Comfort with ambiguity and willingness to learn in public, prototype early, and fail forward.
Excellent written and verbal communication skills.

Nice To Haves

Deep, current knowledge of ML literature and connections with the ML community, such as conference publications, presentations, or collaborations.
Experience, knowledge, and understanding of one or more fields of biology.
Experience working with biological data such as molecular data (DNA, RNA, protein), imaging data (radiology, microscopy), or clinical text (EHR, clinical notes).
Experience in drug development, including clinical trial design, biomarker discovery, companion diagnostics, dosing, safety, endpoints, or regulatory work.
Experience in a matrixed global organization spanning multiple sites and therapy areas.

Responsibilities

Define and drive the AI strategy and roadmap for Oncology early- and late-phase clinical development, aligning AI/ML priorities with clinical and business objectives.
Lead delivery of complex, high-stakes AI projects through matrix influence and scientific authority.
Evaluate, develop, and champion end-to-end AI methods, 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 in 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, and contribute first- or last-author publications in leading ML and clinical AI journals.
Establish best practices and help shape and promote team culture.
Mentor and support more junior scientists on the team.