Director of AI Research, AI for Oncology Clinical Development

AstraZeneca plc; Kendall Square (MA); Presencial; USD 180.000 - 240.000; Jornada completa

Descripción de la vacante

AstraZeneca plc seeks a Director to lead the AI strategy across Oncology clinical development, uniting data, AI tech, and business impact. You will build enterprise-wide, reusable AI capabilities, mentoring senior scientists and influencing cross-functional teams globally.

The role focuses on deploying AI across late and early phase oncology programs, collaborating with biometrics, regulatory, and study teams to embed AI into study design and decision making.

Formación

• PhD in a quantitative field with 4–8 years post-PhD measurable impact.
• Proven ability to deliver impactful ML projects and publications.
• Experience with biological data beneficial.

Responsabilidades

• Define and drive AI strategy and roadmap for Oncology development.
• Lead complex high-stakes AI projects with scientific authority.
• Evaluate and champion cutting-edge AI methods end-to-end.

Conocimientos

Machine learning expertise

Cross-functional collaboration

Scientific leadership

Mentor junior scientists

Strategic thinking

Educación

PhD in quantitative discipline

Herramientas

Python

ML frameworks

Frontier coding frameworks

CUDA knowledge

Descripción del empleo

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;
  • Training and tuning foundation models
  • Bayesian inference
  • Temporal modeling
  • Multimodal integration and modeling
  • Model calibration and domain adaptation
  • Data-centric AI: acquiring, creating, and curating datasets for model training / post-training / benchmarking / evals
  • Model and data evaluations and benchmarking
  • Model interpretability
  • Model post-training and alignment
  • 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 diagno