Director, AI & Data Partner Evaluation

We're building a connected, end-to-endEnterprise AIengine - 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 needs.

• Maintain current knowledge of the competitive landscape, including how peer companies are sourcing and leveraging external data, and where white-space opportunities exist.

3. Align Data Partnerships to TA Evidence Priorities

• Partner directly with TA RWE Strategy Leads to understand prioritized evidence needs and translate them into data sourcing requirements.

• Support the TA Multimodal and Computational Analytics pods by identifying datasets that enable advanced analytics — including molecular data for foundation models, imaging data for analytics, and longitudinal RWD for clinical trial simulation.

• Inform platform development by ensuring data partnerships are compatible with internal tools and agentic AI workflows.

• Participate in TA evidence planning cycles to anticipate future data needs 6–12 months ahead of delivery timelines, ensuring partnerships are in place before evidence generation begins.

4. Drive Partnership Governance and Value Realization

• Establish standardized evaluation and governance processes — including cross-functional scoring (with RWE, Data Science, Legal, Privacy, Procurement, and Finance), KPI frameworks, and regular performance reviews.

• Own renewal and expansion decisions — assess whether existing partnerships continue to deliver value, identify opportunities to extend access across TAs or geographies, and recommend consolidation or termination where appropriate.

• Track and report partnership ROI — quantify impact in terms of evidence generated, cost avoidance, trial design decisions informed, and regulatory submissions supported.

• Manage financial oversight — budget tracking, invoicing, and contract renewals while ensuring compliance with data privacy regulations and ethical data-handling practices.

5. Support Transaction Execution and Mobilization

• Work with procurement, legal, compliance, and finance to support due diligence, contracting, operating model design, and governance set-up for selected partnerships.

• Define partnership objectives, success metrics, decision rights, resourcing requirements, and implementation milestones to ensure disciplined handover from evaluation to delivery.

• Negotiate data access terms that balance scientific utility with privacy, compliance, and commercial constraints.

6. Contribute to Enterprise Data and AI Strategy

• Inform the build-integrate-license framework — advise on when to license external data vs. build internal capabilities vs. integrate with existing platforms.

• Support the cross-TA generalization agenda — identify data assets that can serve multiple therapeutic areas and enable the sequential or parallel development patterns described in the AITC operating model.

• Stay current on the evolving landscape — regulatory expectations for RWE (EMA, FDA), emerging data modalities, privacy frameworks, tokenization, federated analytics, and AI-enabled data curation.

• Contribute to internal capability building — share knowledge on data sourcing best practices, partner evaluation methods, and emerging data modalities with broader AITC teams.

Essential Skills and Experience

• Bachelor's degree or above in epidemiology, biostatistics, health informatics, data science, life sciences, or a related quantitative field. Advanced degree (MSc, MPH, PhD) preferred.

• 7+ years of experience in RWE, health data science, epidemiology, or data strategy roles within pharma, biotech, healthtech, or a major RWD provider — with a demonstrable track record of personally conducting technical data assessments (not solely managing vendor relationships).

• Hands-on technical ability to assess data quality, completeness, representativeness, variable availability, coding systems (ICD, SNOMED, NDC, OMOP, FHIR), linkage feasibility, and fitness-for-purpose against specific research questions.

• Understanding of multimodal data — including structured clinical data, molecular/genomic data, imaging, digital biomarkers, and patient-reported outcomes — and how these can be integrated for advanced analytics and AI/ML applications.

• Existing relationships with data providers that have been built through hands-on technical evaluation and collaborative p...