Senior Director, Data Project Leadership in Cambridge

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.

Introduction to the role

The Senior Director, Data Project Leadership unifies enterprise delivery, governance and value realisation for priority data initiatives. The role leads a portfolio that delivers secure, scalable and high‑quality data capabilities; aligns on shared outcomes with platform, domain and compliance teams; and ensures adoption and benefits are achieved—turning enterprise data strategy into measurable business impact without over‑emphasising any single programme. The role holds enterprise decision rights for programme prioritisation, portfolio trade‑offs and cross‑platform dependency resolution, ensuring coherent delivery across regions and functions.

Scope of accountability:

• Data project leadership: Lead all aspects of delivery of enterprise data projects and capabilities (e.g., expansion of enterprise data products, standards and controls enablement), ensuring timely, on‑budget, high‑quality outcomes and measurable value realisation.
• Portfolio governance and prioritisation: Establish and chair portfolio governance; set prioritisation criteria, arbitrate investment trade‑offs and 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 for cross‑functional delivery teams; staff programmes and manage partner/vendor ecosystems; maintain integrated plans, critical paths and 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, directional proposals and business cases for enterprise data initiatives; articulate value, stakeholder alignment, resources and milestones to secure investment and go/no‑go decisions, including a clear benefits hypothesis, delivery plan and value realisation approach.
• You have owned a multi‑year enterprise delivery roadmap with clear annual OKRs spanning platforms, data products, standards and controls enablement; align with Enterprise Data Programmes strategy and broader AZ objectives.
• Evaluate and recommend delivery approaches and emerging practices that balance speed, quality, risk and 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 and GxP (where applicable) and adherence to enterprise standards.
• Codify and enforce a delivery framework (stage gates, quality gates, design reviews, readiness criteria) that is audit‑ready and consistently applied across programmes.
• Define operating models, roles and ways of working for cross‑functional delivery; staff programmes and manage partner/vendor ecosystems, including performance and commercial oversight.
• Plan and execute end‑to‑end delivery for enterprise data projects, maintaining scope, schedule and budget discipline through codified gates and checkpoints.
• Proactively manage inter‑dependencies across initiatives and with enterprise platforms and domain product teams; maintain integrated plans and critical paths; resolve conflicts and remove blockers.
• Own executive escalations and decisions to remove cross‑functional blockers; resolve conflicts of priority across domains and platforms.
• Implement robust risk, issue, change‑control, quality and benefits tracking processes; run performance reviews, readiness assessments and post‑implementation evaluations.

Value realisation, change and 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, productivity and cost avoidance) and course‑correct to deliver outcomes.
• Lead a short, time‑bound assignment to advance priority AI and data use cases in clinical and human data, ensuring locally initiated work connects into global data, governance and delivery pathways for scale (including alignment with enterprise platforms). Use insights to refine local‑to‑global interfaces and demonstrate how outcomes can be delivered across regional and organisational boundaries.
• Make benefits and value realisation evidence a gate for scale‑up and transition to steady‑state operations.
• Partner with the Data change management pillar to lead stakeholder mapping, communications, readiness and reinforcement; embed behaviours using agreed incentivisation structures and behavioural science‑informed interventions.
• Coordinate with Finance to plan and track budgets, benefits and productivity impacts; evidence value realisation in executive forums.

Collaboration with Data automation:

• Identify where 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 and policy‑as‑code controls into delivery plans.
• Align delivery milestones with automation pilots and scale‑ups; co‑manage transitions to steady‑state operations with platform and domain teams; ensure continuous assurance and monitoring are embedded.

Essential skills and experience:

• Business or scientific degree, with equivalent experience leading enterprise‑level strategy and delivery.
• Significant programme leadership at the intersection of data, AI, automation and pharma R&D / biotech, with a demonstrable record of delivering enterprise‑scale, multi‑year transformations.
• 5+ years’ leadership of complex, multi‑programme portfolios, including prioritisation, investment trade‑offs and cross‑platform dependency management, with clear accountability for value realisation.
• Proven success taking complex data and AI initiatives from vision through to sustained value, including definition and implementation of operating models adopted at scale.
• Deep, demonstrable expertise in programme governance and delivery discipline, including risk and issue management, quality gates, benefits tracking, and audit‑ready execution.
• Accountability for large, multi‑vendor ecosystems, including partner selection, commercial structuring, performance management and risk ownership.
• Strong ability to translate between technical delivery (platforms, data products, standards, controls) and business outcomes; confident communicator with executive‑level storytelling capability.
• Track record of building trusted relationships and influencing outcomes across complex, senior stakeholder landscapes, including executive leadership.
• Experience leading and developing diverse, distributed delivery teams, with responsibility for material budgets and supplier spend.
• Demonstrated delivery of sustained improvements in adoption, time‑to‑value and compliance across enterprise environments.

Desirable:

• Post‑graduate degree or equivalent experience in IT, Data Science, Data Management or 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, storing and accessing personal data.
• Experience embedding policy‑as‑code and 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 challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not 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!