Head of Artificial Intelligence – ICC

AstraZeneca•Waltham, MA

6d•Hybrid

About The Position

AstraZeneca is creating a new leadership role to consolidate and direct AI across Cell Therapy Discovery and Targeted Immune Engagers. Based in the United States (GTB or BOS), the United Kingdom (Cambridge) or the Netherlands (Amsterdam), you will compose the AI strategy, lead delivery of a high-value portfolio, and embed AI as a core capability powering our next wave of medicines. As a member of the CTD and TIE leadership teams reporting to the SVP for IO Discovery and Cell Therapy Oncology, you will set direction, mobilize talent, and deliver measurable impact across discovery and operations. This is a hands-on, build-and-scale mandate. You will form a centralized group of AI experts embedded with R&D teams, orchestrate initiatives from agentic knowledge hubs to predictive CAR-T models and in silico binder design, and establish the governance and operating rhythm that turns prototypes into durable platforms and outcomes.

Requirements

Advanced degree (Master’s or PhD) in Computer Science, Engineering, Mathematics, or a related quantitative field.
Demonstrated 10+ years of experience successfully leading high-performing AI teams and sophisticated AI programs, ideally in life sciences, technology, or R&D-driven environments.
Strategic skill in shaping, scaling, and transforming AI activities for maximum business and scientific impact.
Expertise in the development and deployment of AI/ML technologies, with proven outcomes in sophisticated, multi-stakeholder environments.
Strong understanding of biology or R&D workflows preferred but not required; ability to translate between technical and scientific teams is essential.
Outstanding organizational, communication, and collaborator engagement skills, including experience communicating/translating sophisticated technical findings and priorities to executive and non-technical partners.
Proven experience building, mentoring, and scaling multi-disciplinary teams comprised of machine learning scientists, AI engineers, and data professionals, distributed across multiple locations and embedded in different R&D teams.
Track record of encouraging a collaborative, innovative, and high-integrity team culture.

Nice To Haves

Direct experience applying AI/ML to cell therapy, protein engineering, immunology, or related modalities.
Demonstrated delivery of one or more: agentic knowledge hubs, CAR-T predictive models, in silico binder generation, TCR affinity maturation workflows, CRISPR off-target analyses, or computational mutagenesis.
Familiarity with LLMs, knowledge graphs, MLOps, and cloud-native platforms; experience integrating these into enterprise environments.
Experience with data governance, model risk management, and compliance practices relevant to R&D and regulated settings.
Success managing multi-site teams and external ecosystems, including vendors, consortia, and academic collaborations.
Portfolio management experience with clear KPI frameworks and budget ownership.

Responsibilities

Define and implement the end-to-end AI strategy across CTD and TIE, aligned to enterprise AI goals, with a clear roadmap for 2026–2027 and beyond.
Prioritize and deliver a focused slate of initiatives including agentic knowledge hubs, predictive modeling for cell therapy, in silico protein and binder design, TCR affinity maturation, CRISPR off-target safety, and next-generation analytics.
Build, test, and scale knowledge hub capabilities that enable collaborative analysis, rapid retrieval of institutional knowledge, and faster, better decisions.
Lead models that optimize CAR-T design and performance, reducing cycle times from hypothesis to validation and improving program selection.
Deploy AI workflows that generate and refine binders and mature affinity, increasing hit quality and reducing experimental burden.
Implement sophisticated off-target workflows to improve safety assessments, strengthen study build, and de-risk pipelines.
Automate research and analytics processes to streamline operations, reduce manual effort, and increase reproducibility across sites and teams.
Orchestrate training that lifts foundational AI literacy and fosters an innovative, high-integrity culture where scientists and engineers co-create solutions.
Build deep collaboration with enterprise AI, platform, and external partners to align standards, share knowledge, and improve resource leverage.
Implement robust governance, regulatory compliance, and budget/resource management; institute KPIs that quantify scientific and operational value.
Translate sophisticated technical insights into clear narratives for executive and non-technical collaborators, shaping R&D strategy and investment decisions.