Senior Director, Data Project Leadership

We'rebuilding 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'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.

Introduction to the role

TheSenior Director, Data Project Leadershipunifies enterprise delivery,governanceand value realisation for priority data initiatives. The role leads a portfolio that delivers secure,scalableandhigh‑qualitydata capabilities; aligns on shared outcomes with platform,domainand compliance teams; and ensures adoption and benefits are achieved—turning enterprise data strategy into measurable business impact withoutover‑emphasisingany single programme. The role holds enterprise decision rights for programme prioritisation, portfoliotrade‑offsandcross‑platformdependency resolution, ensuring coherent delivery across regions and functions.

Scope of accountability:

You will lead the Data project leadership pillar within Enterprise Data Programmes:

• Data project leadership: Lead all aspects of delivery of enterprise data projects and capabilities (e.g., expansion of enterprise data products,standardsand controls enablement), ensuringtimely,on‑budget,high‑qualityoutcomesand measurable value realisation.

• Portfolio governance and prioritisation: Establish and chair portfolio governance; set prioritisation criteria, arbitrate investmenttrade‑offsand ensure “compliance by design” through partnership with Compliance, Data Privacy, Quality and (where applicable)GxP; proactively manage dependencies with enterprise platforms, domain product teams and the Data change management and Data automation pillars.

• Operating model and teams: Define roles and ways of working forcross‑functionaldelivery teams; staff programmes and manage partner/vendor ecosystems;maintainintegrated plans, criticalpathsand release calendars across regions. Accountable for the allocation of internal capacity and external partner budgets across the portfolio, including vendor selection and performance management.

Key accountabilities:

Strategic leadership:

• Develop and present delivery visions, directionalproposalsand business cases for enterprise data initiatives; articulate value, stakeholder alignment, resources and milestones to secure investment and go/no‑godecisions, including a clear benefits hypothesis, deliveryplanand value realisation approach.

• You have ownedamulti‑yearenterprise delivery roadmap with clear annual OKRs spanning platforms, data products,standardsand controls enablement; align with Enterprise Data Programmes strategy and broader AZobjectives.

• Evaluate and recommend delivery approaches and emerging practices that balance speed, quality,riskand cost; translate internal and industry trends into executable delivery strategy.

• Align delivery approaches to enterprise risk appetite and control frameworks, ensuring pace without compromising assurance.

Programme execution and governance:

• Establish and chair portfolio governance; ensure “compliance by design” with privacy, security andGxP(where applicable) and adherence to enterprise standards.

• Codify and enforce a delivery framework (stage gates, quality gates, design reviews, readiness criteria) that isaudit‑readyand consistently applied across programmes.

• Define operating models,rolesand ways of working forcross‑functionaldelivery; staff programmes and manage partner/vendor ecosystems, including performance and commercial oversight.

• Plan and executeend‑to‑enddelivery for enterprise data projects,maintainingscope, schedule and budget discipline through codified gates and checkpoints.

• Proactively manageinter‑dependenciesacross initiatives and with enterprise platforms and domain product teams;maintainintegrated plans and critical paths; resolve conflicts and remove blockers.

• Own executive escalations and decisions to removecross‑functionalblockers; resolve conflicts of priority across domains and platforms.

• Implement robust risk, issue,change‑control, quality and benefits tracking processes; run performance reviews, readinessassessmentsandpost‑implementationevaluations.

Value realisation,changeand culture:

• Define value hypotheses and success metrics from pilot through scale; track benefits (e.g., adoption, cycle time, data quality defect rates, reduction in compliance findings,productivityand cost avoidance) andcourse‑correctto deliver outcomes.

• Lead a short,time‑boundassignment to advance priority AI and data use cases in clinical and human data, ensuring locally initiated work connects into global data,governanceand delivery pathways for scale (including alignment with enterprise platforms). Use insights to refinelocal‑to‑globalinterfaces anddemonstratehow outcomes can be delivered across regional and organisational boundaries.

• Make benefitsand valuerealisation evidence a gate forscale‑upand transition tosteady‑stateoperations.

• Partner with the Data change management pillar to lead stakeholder mapping, communications,readinessand reinforcement; embed behaviours using agreed incentivisation structures and behaviouralscience‑informedinterventions.

• Coordinate with Finance to plan and track budgets,benefitsand productivity impacts; evidence value realisation in executive forums.

Collaboration with Data automation:

• Identifywhere automation accelerates delivery or improves quality/compliance (e.g.,policy‑as‑code, continuous assurance); partner with the Data automation pillar to integrate reusable automation patterns andpolicy‑as‑codecontrols into delivery plans.

• Align delivery milestones with automation pilots andscale‑ups;co‑managetransitions tosteady‑stateoperations with platform and domain teams; ensure continuous assurance and monitoring are embedded.

Essential skills and experience:

• Business or scientific degree, with equivalent experience leadingenterprise‑levelstrategy and delivery.

• Significant programme leadership at the intersection of data, AI,automationand pharma R&D / biotech, with a demonstrable record of deliveringenterprise‑scale,multi‑yeartransformations.

• 5+ years’ leadership of complex,multi‑programmeportfolios, including prioritisation, investmenttrade‑offsandcross‑platformdependency management, with clear accountability forvalue realisation.

• Proven success taking complex data and AI initiatives fromvision through to sustained value, including definition and implementation of operating models adopted at scale.

• Deep, demonstrableexpertiseinprogramme governance and delivery discipline, including risk and issue management, quality gates, benefits tracking, andaudit‑readyexecution.

• Accountability forlarge,multi‑vendorecosystems, including partner selection, commercial structuring, performancemanagementand risk ownership.

• Strong ability totranslate between technical delivery (platforms, data products, standards, controls)and business outcomes; confident communicator withexecutive‑levelstorytelling capability.

• Track recordof building trusted relationships and influencing outcomes acrosscomplex, senior stakeholder landscapes, including executive leadership.

• Experience leading and developingdiverse, distributed delivery teams, with responsibility formaterial budgets and supplier spend.

• Demonstrated delivery ofsustained improvements in adoption,time‑to‑valueand complianceacross enterprise environments.

Desirable:

• Post‑graduatedegree or equivalent experience in IT, Data Science, DataManagementor related automation subject areas.

• Strong Direct experience in pharmaceutical R&D and global organisations.

• Detailed knowledge of patient data types and their use in drug development.

• Understanding of regulations and compliance for processing,storingand accessing personal data.

• Experience embeddingpolicy‑as‑codeand continuous assurance into data delivery.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challengeperceptions.That'swhy we work, on average, a minimum of three days per week from the office. But thatdoesn'tmeanwe'renot flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

AstraZeneca offers an environment where data, analytics and AI are central to transforming how medicines are discovered, developed and delivered; where partnerships across global functions drive efficiency; where modern platforms are already in place and ready to be leveraged; where experimentation with leading-edge technology is encouraged; where diverse experts collaborate across boundaries; where learning never stops; and where every improvement in how information is governed can ultimately help improve outcomes for patients worldwide.

If this sounds like the next challenge to own and shape, apply now to join us!

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