Associate Director, AI for Oncology Clinical Development
AstraZeneca · Cambridge Kendall SQ, MA
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Role summary
Associate Director, AI for Oncology Clinical Development at AstraZeneca leads technical strategy and delivers AI solutions to accelerate and optimize clinical drug development across oncology programs. The role combines machine learning expertise with clinical development knowledge to advance late-stage pipeline decisions and study design.
Where this listing comes from
This listing was compiled by RoleLantern from the employer's publicly available posting on Workday, captured and rephrased by RoleLantern using automated (AI) tools on 2026-10-06 (originally posted 2026-10-06). It was not submitted or confirmed by the employer through RoleLantern. Details may be inaccurate, incomplete, outdated, or no longer available; please confirm with the employer's official posting before applying.
What they're looking for
- PhD in quantitative discipline (computer science, bioinformatics, computational biology, mathematics, physics, or related field)
- 2+ years post-PhD work experience with measurable impact (deployed models, patents, publications, or standards contributions)
- Exceptional software development and coding skills, including frontier coding frameworks
- Deep knowledge of machine learning fundamentals and domain expertise in foundation models, Bayesian inference, temporal modeling, or model interpretability
- Strong understanding of biology and experience with biological data (molecular, imaging, or clinical text)
- Preferred: cancer biology expertise and drug development experience (clinical trial design, biomarkers, regulatory knowledge)
What you'll be doing
- Contribute to AI strategy and roadmap for oncology early and late-phase clinical development
- Lead technical delivery of complex, high-stakes AI projects across cross-functional matrixed teams
- Evaluate and develop cutting-edge AI methods for problem definition, data governance, algorithm development, and validation
- Partner with clinical, biometrics, regulatory, and study teams to develop and validate AI solutions into clinical practice
- Establish external collaborations with academic institutions and technology partners; publish in peer-reviewed venues
Key requirements
- Therapeutic area
- Oncology
Privacy & handoff
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