Job Title: Director, AI Research
Introduction to role:
Are you ready to bring frontier AI into the heart of clinical development and change how trials are designed, conducted, and analyzed? Can you move seamlessly from a clinical question to the AI system that answers it and stands up to scientific and regulatory scrutiny?
In this director-level role, you will define and deliver the AI research agenda that improves decisions for trials and patients across Oncology and BioPharmaceuticals. You will work at the intersection of advanced AI, clinical trial methodology, and biomedical science—translating methodological innovation into capabilities that shape clinical-development strategy and outcomes.
You will lead research spanning foundation models, multimodal learning, agentic systems, post-training methods, and evaluation science. Your work may address treatment-effect heterogeneity, predictive biomarker discovery and validation, patient stratification, time-to-event outcomes, cross-trial generalization, and the responsible evaluation and governance of AI systems used in developing new medicines.
Accountabilities:
- Research Strategy and Roadmap: Set and execute a research strategy focused on the highest-impact clinical-development challenges, prioritizing efforts that drive measurable benefits for patients and the pipeline.
- Method Development for Trials: Invent and refine AI and statistical learning methods tailored to randomized clinical trials and related evidence, with emphasis on heterogeneous treatment effects, predictive biomarkers, time-to-event outcomes, patient stratification, and cross-trial generalization.
- Agentic AI Systems: Design, build, and rigorously evaluate agentic AI systems for clinical-development use cases, incorporating retrieval, tool use, code execution, memory, multi-step workflows, and human oversight to ensure reliability and reproducibility.
- Reproducible Engineering: Write high-quality Python and build reproducible research workflows using modern ML frameworks, version control, testing practices, and cloud or high-performance computing environments to accelerate trustworthy science at scale.
- Scientific Leadership and Influence: Serve as a senior scientific and technical partner, shaping clinical strategy, evaluation approaches, platform capabilities, and investment decisions across the organization.
- External Dissemination and Partnerships: Share research through peer-reviewed publications, reusable tools, patents, scientific collaborations, and conference presentations; represent the company with partners, consortia, regulators, and the broader research community.
- Team Culture and Mentorship: Cultivate a rigorous research culture through structured scientific review, constructive challenge, and mentorship of scientists and engineers, raising the bar for impact and integrity.
- Responsible AI and Governance: Establish robust evaluation, governance, and responsible-use frameworks for advanced AI systems in clinical development, aligning innovation with patient safety and regulatory expectations.
Essential Skills/Experience:
- Hold a PhD in Computer Science, Machine Learning, Biostatistics, Biomedical Informatics, Computational Biology, or a closely related quantitative field, or have an MSc with an equivalent record of research and industry impact.
- Eight years of experience in original research and technical impact, demonstrated through influential publications, clinical evidence, deployed systems, patents, widely used software or datasets, or comparable contributions.
- Have led substantial research programs end to end and made scientific or technical decisions that shaped a broader portfolio, platform, or organizational direction.
- Are comfortable setting direction under uncertainty, challenging assumptions, and influencing senior partners in our organization
- Bring expertise in relevant research topics, such as unsupervised learning, dimensionality reduction, causal inference, survival or longitudinal analysis, Bayesian modeling, multimodal learning, domain adaptation, model interpretability.
- Are comfortable working with AI agent harnesses, quick to pick up new approaches, and curious about what makes agents useful, reliable, and safe.
- Have strong Python and scientific-computing skills and experience with a modern ML framework such as PyTorch, JAX, or TensorFlow.
- Can explain technical findings, tradeoffs, and limitations to clinical, statistical, regulatory, engineering, and leadership audiences.
Desirable Skills/Experience:
- Experience with biomedical data such as clinical text, EHR or trial data, biomedical imaging, genomics, transcriptomics, spatial biology, or real-world data.
- Hands-on experience with large language models or agentic systems, including fine-tuning or post-training, retrieval-augmented generation, tool use, agent harnesses, multi-step orchestration, evaluation, or inference and serving.
- Familiarity with clinical data standards, regulated validation, GxP, software as a medical device, model-risk management, or interactions with health authorities.
- Contributions to scientific consortia, reporting or evaluation standards, open-source communities, or other work that has influenced practice beyond a single project.
- Experience mentoring scientists or engineers and providing technical leadership in a global, matrixed organization.
The annual base pay for this position ranges from $186,014 to $279,021 USD. Our positions may be eligible for various incentives, including short-term incentive bonuses, equity-based awards for salaried roles, and commissions for sales roles. Benefits may include qualified retirement programs, paid time off—including vacation, holidays, and leave—as well as health, dental, and vision coverage, in accordance with the terms of the applicable plans.
AstraZeneca is an equal opportunity employer committed to diversity and inclusion and to providing a workplace free from discrimination. AstraZeneca is committed to accommodating persons with disabilities. Accommodation is available upon request for all aspects of the recruitment, assessment, and selection process. Requests may be made by emailing [email protected].
Why AstraZeneca:
Join a company where advanced technology meets rigorous science to accelerate the delivery of life-changing medicines. You will collaborate with clinicians, statisticians, engineers, and data scientists in an environment that brings unexpected combinations of talent together to spark bold ideas. We invest deeply in learning and development—through coaching, feedback, and hands-on experimentation—so you can grow as fast as the field evolves. We pair ambition with care, valuing kindness alongside high standards, and expect every colleague to embrace AI and digital tools in day-to-day work. We thrive on in-person collaboration and typically spend at least three days a week together on site, while balancing individual flexibility, to move quickly on the problems that matter most to patients.
Call to Action:
If you are ready to lead a cutting-edge AI research agenda that shapes clinical decisions and delivers tangible benefits for patients, seize this opportunity to build what’s next and amplify your impact.
#LI-Hybrid
Date Posted
27-Oct-2026Closing Date
29-Nov-2026Our 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.
Skills Required
- PhD in Computer Science, Machine Learning, Biostatistics, Biomedical Informatics, Computational Biology, or a closely related quantitative field; alternatively, an MSc with equivalent research and industry impact.
- Eight years of experience in original research and technical impact.
- Experience leading substantial research programs end to end and shaping portfolio, platform, or organizational direction.
- Expertise in relevant research areas such as causal inference, survival or longitudinal analysis, Bayesian modeling, multimodal learning, model interpretability, or unsupervised learning.
- Strong Python and scientific-computing skills.
- Experience with PyTorch, JAX, TensorFlow, or another modern machine-learning framework.
- Ability to explain technical findings, tradeoffs, and limitations to clinical, statistical, regulatory, engineering, and leadership audiences.
- Experience with biomedical data, including clinical text, EHR, trial data, biomedical imaging, genomics, transcriptomics, spatial biology, or real-world data.
- Hands-on experience with large language models or agentic systems, including fine-tuning, retrieval-augmented generation, tool use, orchestration, evaluation, inference, or serving.
- Familiarity with clinical data standards, regulated validation, GxP, software as a medical device, model-risk management, or health-authority interactions.
- Contributions to scientific consortia, reporting or evaluation standards, open-source communities, or practice-influencing work.
- Experience mentoring scientists or engineers in a global, matrixed organization.
AstraZeneca Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AstraZeneca and has not been reviewed or approved by AstraZeneca.
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Fair & Transparent Compensation — Pay is considered competitive across many roles when total rewards are factored in. Senior scientific and leadership bands are described with high ranges that reinforce competitiveness at upper levels.
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Strong & Reliable Incentives — Bonuses, equity eligibility in many salaried roles, and solid sales on‑target earnings with upside are emphasized as meaningful parts of compensation. These elements boost overall value even where base pay is not the very highest.
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Retirement Support — A 401(k) program with a strong company match and immediate vesting is repeatedly cited as a standout. Generous retirement support is viewed as enhancing the total package relative to peers.
AstraZeneca Insights
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