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Director, Data Scientist - Clinical AI

Trabajo disponible en: 08001, Barcelona, Cataluna, España
Empresa: AstraZeneca GmbH
Tiempo completo puesto
Publicado en 2026-09-13
Especializaciones laborales:
  • TI/Tecnología
    Ingeniero de IA, Machine Learning, Científico de datos
Rango Salarial o Referencia de la Industria: 120000 - 180000 EUR Anual EUR 120000.00 180000.00 YEAR
Descripción del trabajo

Location

Barcelona - Spain or Cambridge - UK (3 days in-office requirement)

About AstraZeneca and AISI

At AstraZeneca,technology and science meet to change what is possible for patients. We are 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 will activelyleverageexisting capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation.

AI Science & Innovation (AISI) sits at thecentreof AstraZeneca's R&D AI transformation. Our remit is to build, buy, and deliver the AI models and agents that change pipeline outcomes across discovery, translational science, biomarkers, and clinical development.

Within AISI, the Clinical AI teamsarebuilding world-class AI capability to accelerate the design, conduct, and analysis of clinical trials across ourBioPharmaceuticalspipelines— spanning both early and late phaseprogrammes. We partner closely with clinical development, regulatory, and biometrics teams to bring better treatments to patients faster, while adhering to the highest evidentiary standards.

The Opportunity

Bringing new treatments to patients demands scientific excellence at every stage of development. In the Clinical AI team, we focus on one of the most data-rich and decision-intensive parts of that journey: clinical development. Trial design, patient selection, doseoptimisation, biomarker strategy, and safety evaluation each represent genuine opportunities where AI and machine learning can addrigour, speed, and precision — not as a replacement for clinical and statistical expertise, but as a powerful complement to it.

We hold ourselves to measurable standards of improvement, and we build methods that can be evaluated, reproduced, and trusted in regulatory settings.

You will work across the enterprise to define and deliver on AstraZeneca's most pressing clinical development questions — leading cross-functional teamsspanning the keyBioPharmaceuticalsdisease areas of cardiovascular, renal, metabolic disease, respiratory,immunology and cell-therapy.

You and the team will develop reusable methods and enterprise-scale approaches that measurably advance the late-stage drug pipeline. This is a high-visibility opportunity to shape how AstraZeneca does AI forBioPharmaceuticalsclinical development — frommethodologystandards to external scientific influence.

AI for clinical development is a field in motion. Foundation models, agentic systems, and causal AI are advancing rapidly, and the regulatory and methodological frameworks around them are evolving in parallel. As a Director, Data Scientist, you will define and drive the AImethodologyagenda for one or moreprogrammeswithin

Clinical AI, leading by scientific influence and matrix coordination rather than through a formal hierarchy. You will be the scientific authority that study teams, biometrics, and regulatory colleagues turn to — and AstraZeneca's voice externally at the critical moment when the rules of the road for AI in clinical trials are being written.

Key Responsibilities
  • Define and drive the AImethodologyroadmap for assigned

    Clinical AIprogrammes, spanning early and late phase clinical development, and aligning AI/ML priorities with clinical and business objectives.

  • Lead, by matrix influence and scientific authority, the delivery of the most complex and high-stakes AI projects — from problem definition andmethodologyselectionthrough validation, regulatory alignment, and scaled adoption across the enterprise.

  • Develop and govern reusable, enterprise-grade AI…

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