Senior Director, Head of AI for Clinical Development, Late Oncology

AstraZeneca • Boston, MA

6d • $203,214 - $304,820 • Hybrid

About The Position

AstraZeneca is building a world-class AI capability for Clinical Development within AISI to accelerate the design, conduct, and analysis of confirmatory trials across our Oncology and BioPharmaceuticals pipeline. Phase III is where assets meet the regulatory and label-evidence bar — and where AI must be useful, defensible, and audit-ready. We are hiring the Head of AI for Clinical Development, Late Oncology, to lead the science and the team that turns AI promise into real-world improvements in late phase clinical trials, including trial enrichment, external evidence, label-relevant endpoints, and submission-ready insight. This is one of the hardest and highest impact challenges in AI for healthcare. The playbook for AI at this stage will be developed in the next two to three years. We partner closely with domain experts in clinical development, regulatory, and biometrics to advance the science of clinical development to bring better treatments to patients, faster, while adhering to the highest evidentiary standards. We’re hiring someone who sees that as the reason to come because they are committed to using AI for real, measurable improvement in healthcare. AI for clinical development is a field in motion. Foundation models, multimodal learning, agentic systems, and causal AI advance rapidly, and the regulatory and methodological frameworks around them are evolving in parallel. You’ll pick the right bets among rapidly changing options, and continuously absorb new methods as the field redefines what’s possible. Comfort with ambiguity and an instinct to learn in public are core to the role. The AI for Clinical Development function is being built from the ground up, and you’ll help define how AstraZeneca does AI for late-phase trials. Expect an outsized voice with regulators, scientific consortia, and external partners during the narrow window when the rules of the road for AI in pivotal evidence are being written. We hire for learning agility and technical excellence. The strongest candidate is the person who learns fast, is comfortable with ambiguity, prototypes early, fails forward, and partners credibly across communities (ML, clinical, biostatistics, regulatory). We’re seeking a scientist with the leadership, technical depth, and curiosity to develop, adapt, and apply the most advanced methods in AI, including multimodal foundation models, agentic AI, generative patient models, to late-phase clinical decision-making, and who can sit across from clinical-development teams to translate their priorities into actionable, innovative, and evaluable AI solutions. Above all, this is a role where the science matters. Every model you ship will eventually touch a trial that decides whether a patient gets a better therapy. That is the bar we hold ourselves to, and the bar we hire to.

Requirements

• PhD in Computer Science, Machine Learning, Computational Biomedical Sciences, Biomedical Informatics, Biostatistics, or a closely related computational discipline — with a strong, hands-on computational track record. Alternatively, MD (or equivalent clinical degree) with significant demonstrated hands-on and leadership experience in computer science/machine learning, preferably with post-doctoral training or other advanced training in computer science, informatics, or data science.
• Minimum 8 years of combined experience across AI/ML method development and clinical development research, with demonstrated impact on late phase trial design and/or decision-making.
• Excellent software engineering skills: Python, PyTorch, Hugging Face, cloud platforms (e.g., AWS, Azure, GCP), and modern LLM tooling.
• Expertise in sandboxing, containerization, VM infrastructure, and/or distributed systems.
• Current, hands-on computational expertise, peer-reviewed publications in top-tier conferences and/journals and shipped code in clinical AI, computational drug development, and/or biomedical machine learning.
• Comfort writing code, reviewing model implementations, and reproducing results.
• Strong experience in generative and non-generative AI benchmarking and evaluation across clinical and/or biomedical settings.
• Deep familiarity with modern AI methods relevant to confirmatory late clinical development: foundation models (including clinical and multimodal), agentic systems, generative patient models / digital twins, longitudinal/time-series modelling, causal inference, and AI methods for safety and pharmacovigilance.
• Knowledge of confirmatory trial design and conduct, and the evidentiary bar that AI in pivotal evidence must clear.
• Demonstrated track record of translating AI methods into applications that inform clinical and/or biomedical decisions, including prospective evaluation, deployment, or contribution to submission-relevant evidence.
• Demonstrated scientific leadership: mentoring trainees, leading multi-author projects, and running a small team overseeing multiple projects.
• Excellent written and verbal communication, able to translate technical findings for clinical, regulatory, and executive audiences.

Nice To Haves

• Direct industry experience in late-phase Oncology, late-phase BioPharm, or confirmatory clinical development within a pharmaceutical or biotech R&D environment.
• Direct experience contributing to FDA, EMA, PMDA, or MHRA submissions — especially submissions involving AI/ML methods, external controls, digital twins, or AI-augmented adaptive designs.
• Experience with hybrid and external-control designs and the AI capability layer that supports them.
• Prior FDA, EMA, or PMDA engagement on AI methodology, complex innovative trial design, or AI/ML qualification opinions.
• First- or last-author publications at top ML venues (NeurIPS, ICML, ICLR, CL) and/or top clinical and biomedical journals.
• Open-source contributions, workshop organisation, or standards-body participation.

Responsibilities

• Lead AstraZeneca’s AI R&D for Phase III programs across oncology, leveraging and integrating AI solutions across the late development program.
• Lead a team of AI researchers and engineers to leverage and integrate AI solutions across late oncology clinical development.
• Develop and evaluate AI methods for innovative trial design (e.g., prognostic risk modelling, predictive enrichment, AI-driven stratification, sample-size and power optimisation, and biomarker-defined population strategy).
• Lead innovative approaches to analyze and quality control real-time clinical trial data from late phase trials.
• Develop and evaluate predictive models, including AI-based analysis of real-world evidence for Phase III trials (e.g., external control arm methodology, predictive and progrnostic models, generative patient trajectory models).
• Develop and evaluate AI methods for multimodal label-relevant endpoints — imaging-based response, digital pathology, ePRO and digital biomarkers, surrogate-to-OS modelling — and for label-shaping analyses (subgroup discovery, individual treatment-effect estimation).
• Partner with the Late Oncology Clinical Development and Study Teams to embed AI and AI Strategy into study design and decision-making.
• Partner with Regulatory teams to develop AI-enabled development standards and evidence that meet late-phase regulatory requirements.
• Work closely with the Heads of Early Oncology and BPRD Clinical AI and the Head of Late BPRD Clinical AI to develop innovative AI strategies to guide early- to late-phase decision-making.
• Contribute to developing the AI for Clinical Development team’s unified, reusable, and end-to-end strategies for AI method development, evaluation, monitoring, and oversight.
• Contribute the AI evidence component to regulatory submission packages and the AI scientific and methodological voice on late-phase regulatory engagements.
• Represent AstraZeneca externally in industry forums, scientific consortia, and peer-reviewed venues.
• Serve as a thought partner for AISI and AI for Clinical Development leadership.
• Recruit, mentor, and lead a team of approximately five AI scientists and engineers as a player-coach who is hands-on with code, models, and submission-relevant analyses while building a high-performing team that keeps up-to-date with the latest advances in frontier AI models and agents.

Benefits

• qualified retirement program [401(k) plan]
• paid vacation and holidays
• paid leaves
• health benefits including medical, prescription drug, dental, and vision coverage

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree