Head of Artificial Intelligence – ICC
About this opportunity
Head of Artificial Intelligence – ICC
Are you ready to build and lead a high-impact AI organization that turns sophisticated biology into decisive action for patients? Can you unite distributed expertise into a single, strategic engine that accelerates discovery and transforms how we work end to end?
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.
Accountabilities:
- Strategic Leadership: 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.- Portfolio Orchestration: 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.- Agentic AI Development: Build, test, and scale knowledge hub capabilities that enable collaborative analysis, rapid retrieval of institutional knowledge, and faster, better decisions.- Predictive Modeling for Cell Therapy: Lead models that optimize CAR-T design and performance, reducing cycle times from hypothesis to validation and improving program selection.- In Silico Protein and Binder Design: Deploy AI workflows that generate and refine binders and mature affinity, increasing hit quality and reducing experimental burden.- CRISPR Safety and Risk: Implement sophisticated off-target workflows to improve safety assessments, strengthen study build, and de-risk pipelines.- Workflow Automation: Automate research and analytics processes to streamline operations, reduce manual effort, and increase reproducibility across sites and teams.- AI Upskilling and Culture: Orchestrate training that lifts foundational AI literacy and fosters an innovative, high-integrity culture where scientists and engineers co-create solutions.- Collaborator Partnership: Build deep collaboration with enterprise AI, platform, and external partners to align standards, share knowledge, and improve resource leverage.- Governance and Value Realization: Implement robust governance, regulatory compliance, and budget/resource management; institute KPIs that quantify scientific and operational value.- Communication and Influence: Translate sophisticated technical insights into clear narratives for executive and non-technical collaborators, shaping R&D strategy and investment decisions.
Essential Skills/Experience:
- 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.
Desirable Skills/Experience:
- 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.
When we put unexpected teams in the same room, we unleash ambitious thinking with the power to encourage life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our outstanding and ambitious world!
Why AstraZeneca:
Here, ground breaking science meets ambitious technology in service of patients. You will join a community that thrives on collaboration across subject areas, where ideas move quickly from exploration to scaled platforms that change how we discover and develop medicines. We put unexpected teams together to unlock new thinking, invest deeply in digital capabilities across the R&D lifecycle, and value kindness alongside ambition so people can take smart risks and learn fast. Your leadership will connect AI breakthroughs to tangible outcomes for patients and programs, while opening new horizons for your own growth.
The annual base pay for this position ranges from $281,872.80 - 422,809.20 USD Annual USD. Our positions offer eligibility for various incentives, an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.
Date Posted
11-Aug-2026
Closing Date
20-Aug-2026 Our 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.
Job details
How this role compares
Computed from every other active Drug Discovery & Preclinical Research role in our database, not just this employer's listings.
We currently track 383 comparable Drug Discovery & Preclinical Research roles across 47 biopharma companies.
Salary context
137 of 383 peers report a salary range (USD, annualized)
Peers share this role's job function. This posting doesn't list a seniority level, so peers aren't narrowed by seniority either -- the range below may span more levels than usual.
Where these roles are based
Top locations among the 383 comparable roles
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Seniority mix
297 of 383 peers have a known seniority level
Therapeutic area mix
54 of 383 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
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Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.