Director, AI & Data Partner Evaluation

About this role

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 "T-shaped" 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 + 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 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.  

• Cultivate deep relationships with strategic partners and emerging providers in multimodal and AI-enabled data — built through hands-on technical evaluation and collaborative problem-solving, not solely through commercial channels.  

• Proactively   identify   new entrants — startups, academic consortia, health system data collaboratives, and government-linked datasets — that could address unmet evidence

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