We'rebuilding 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'llactivelyleverageexisting 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.

This role sits at the intersection of AstraZeneca's Clinical Intelligence and RWE teams and the rapidly evolving external ecosystem of AI/ML platform companies, foundation model developers, multimodal analytics partners, and real-world data providers. The Data & AI Partnerships Lead will ensure that therapeutic area teams across Oncology (Lung/HNSCC, Women's Cancer, GI/GU, Haematology) and Biopharmaceuticals (CVRM, Respiratory, Immunology, Infectious Disease) can access, evaluate, and mobilize the right external capabilities — whether those are foundation models, computational platforms, agentic AI tools, or datasets — to power evidence generation, multimodal analytics, and AI-enabled clinical decision-making.

The successful candidate will be a "T-shaped" technical operator — deep in AI/ML and computational partner evaluation, with sufficient breadth in real-world data toinitiateand frame data assessments before handing off to TA RWE experts for deep validation. This is not a traditional business development role. In a typical week,



this person might be:

- Evaluating a multimodal foundation model partner's approach to integrating imaging, genomic, and clinical data for patient stratification in lung cancer
- Assessing whether an agentic AI platform's orchestration capabilities are compatible with the team's infrastructure
- Initiating a fit-for-purpose review of a new molecular data provider — scoping the key questions, runningan initialcompleteness check, and then handing the detailed variable-level assessment to the Lung or GI/GU RWE Strategy Lead for domain-specific validation
- Briefing senior stakeholders on a build-vs-license recommendation for a clinical trial simulation capability

The right candidate will build their network through hands‐on technical collaboration with AI and data partners and will be as comfortable interrogating a model's trainingmethodologyand validation evidence as they are framing a data quality question for a TA expert to resolve.

Role Scope

- Technical Data Assessment: Hands-on evaluation of external datasets against specific evidence questions — assessing volume, completeness, representativeness, variable availability, linkage capability, latency, coding standards, and regulatory acceptability.
- Data Partner Scouting & Network: Maintainand expand a curated network of RWD, genomic, imaging, claims, EHR, registry,



and digital health data providers relevant to Oncology andBiopharmaceuticalsevidence needs.
- TA Evidence Alignment: Partner with RWE Strategy Leads, Multimodal Analytics Leads, and Data Scientists across TAs to translate evidence gaps into data sourcing requirements.
- Partnership Lifecycle: Own end-to-end data partnership management from scouting and pilot evaluation through contracting, onboarding, performance governance, renewal, and expansion aligned to the needs of the business.
- Cross-TA Data Strategy: Identifyopportunities toleveragea single data partnership across multiple therapeutic areas, maximizing value and reducing duplication.
- Regulatory & Compliance Alignment: Ensure all proposed data partnerships meet privacy, ethical governance, and regulatory-grade evidence standards (EMA RW-DQF, FDA RWE guidance, GDPR, HIPAA).

Key Accountabilities 1. Originate and Qualify Strategic Partnerships

- Lead fit-for-purpose evaluations using a structured framework covering:volume/depth, reliability/completeness, usability/interoperability, linkage potential, and regulatory compliance.
- Design and execute pilot analyses to stress-test data quality, variable availability, coding accuracy, and cohort feasibility before recommending full-scale agreements.
- Assess multimodal integration potential — evaluate whether partner datasets can be linked to internal assets or other external sources (e.G., EHR + genomic + imaging) to support the multimodal classifiers and AI/ML models being developed by TA Computational Analytics teams.
- Provide technical opinions on data suitability for specific use cases including external control arms, trial emulation, biomarker validation, patient stratification, site identific

📌 Partnership Director (Cataluña)
🏢 AstraZeneca
📍 Cataluña