Director, AI & Data Partner Evaluation

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. 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 &#34;T-shaped&#34; technical operator — deep in AI/ML and computational partner evaluation, with sufficient breadth in real-world data to initiate and 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, running an initial completeness 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 training methodology and 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: Maintain and expand a curated network of RWD, genomic, imaging, claims, EHR, registry, and digital health data providers relevant to Oncology and Biopharmaceuticals evidence 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: Identify opportunities to leverage a 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 &#43; genomic &#43; 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 identification, and efficacy benchmarking. Develop and maintain standardized assessment templates — including data dictionaries, completeness scorecards, representativeness benchmarks, and regulatory-readiness checklists — that can be applied consistently across partnership evaluations. 2. Build and Maintain a Best-in-Class Data Partner Network Develop a dynamic landscape map of external data providers across claims, EHR, molecular/genomic, imaging, patient-reported outcomes, digital biomarkers, and linked datasets — organized by therapeutic relevance, geography, and data modality. <