Senior Director, Global Commercial Data, AI & Reporting Excellence
Reports to: VP, Global Insights & Business Excellence (GIBEx), BBU
Scope: Global | Commercial data, AI, digital platforms and performance reporting | Approximately five direct reports plus matrix delivery teams
Role purpose
The Senior Director sets the enterprise direction for how BBU commercial data, AI and reporting capabilities are designed, governed and scaled. The role owns the business strategy and value of key commercial data products and decision platforms, ensuring that trusted data and responsible AI improve decision quality, speed, productivity and commercial outcomes.
This is a senior business and technology savvy leadership role. The role translates commercial priorities into an AI-enabled data and digital roadmap; establishes clear product ownership, architecture principles, governance and reusable standards; and partners with Commercial IT, the Commercial Data Office, Finance, Enterprise AI teams, Operations, Insights, Analytics and Forecasting, global brand teams, regions and markets to deliver adoption and measurable value.
The portfolio includes pharmaceutical secondary market data and healthcare professional engagement data, together with the products, semantic definitions, interfaces, analytics and reporting platforms that make these assets usable. The role leads Data Product Owners and Platform Leads and is accountable for talent, delivery quality, investment choices, risk and value realisation.
Key responsibilities
- Set strategy and investment priorities: Define the multi-year vision and roadmap for commercial data, AI, reporting and digital decision platforms, aligned to BBU and GIBEx priorities.
- Own the business data architecture: Establish target-state principles for data domains, products, models, metadata, taxonomy, lineage, interoperability and access. Partner with Commercial IT on the technical architecture and prevent fragmented or duplicative solutions.
- Lead AI-enabled work redesign: Identify where predictive, generative and agentic AI can improve decisions and workflows; prioritise use cases by value, feasibility and risk; and move successful solutions from experiment to governed, scalable products.
- Ensure responsible AI: Embed human accountability, validation, explainability, privacy, security, bias controls, monitoring and auditability across the AI lifecycle, in line with applicable policies and regulation.
- Lead the data-product portfolio: Own product vision, outcomes, lifecycle, roadmaps and backlogs for commercial data products and platforms. Apply user discovery and product management to anticipate and solve priority business problems rather than produce outputs on request.
- Create trusted data: Establish clear ownership, quality standards, critical data definitions, controls and issue-resolution routes for pharmaceutical secondary data and HCP engagement data.
- Modernise reporting and decision support: Simplify and standardise enterprise KPIs, reporting and executive performance views; increase reuse and self-service; and retire low-value or duplicative products.
- Deliver measurable value: Define and track adoption, decision impact, productivity, quality, risk and return on investment. Use evidence to stop, redesign or scale work.
- Orchestrate enterprise delivery: Align GIBEx, global brands, regions, markets, Commercial IT, the Commercial Data Office, Finance, AI teams and Operations around shared priorities, decision rights, standards and delivery plans.
- Lead talent and partners: Build a high-performing, globally distributed team; develop AI, product, data and commercial capability; manage vendors, budgets, capacity and delivery risk; and create clear accountability across direct and matrix teams.
- Advise senior leaders: Translate complex data and technology choices into clear business options, trade-offs, recommendations and risks for executive decisions.
Essential experience and skills
- Significant senior leadership experience in commercial data, analytics, AI, digital products or data platforms within pharmaceuticals, life sciences or another complex, regulated sector.
- Demonstrated ownership of an enterprise data and AI strategy, with evidence of moving products from concept through deployment, adoption, operation and measurable value.
- Strong working knowledge of modern data architecture, including domain-based data products, cloud data platforms, integration patterns and APIs, semantic layers, master and reference data, metadata, lineage, identity and access, and structured and unstructured data.
- Strong AI literacy across machine learning, generative AI and agentic workflows, including model and use-case evaluation, human-in-the-loop design, retrieval approaches, monitoring and responsible AI controls. The role does not require hands-on model coding but does require credible technical judgement.
- Expertise in data-product and platform management: customer discovery, product vision, roadmaps, prioritisation, lifecycle ownership, service levels, adoption and value measurement.
- Deep understanding of data governance, quality, privacy, security, regulatory expectations and third-party risk in a regulated environment.
- Knowledge of pharmaceutical commercial data, including secondary sales and market data, claims or real-world data, and HCP/customer engagement across digital and physical channels.
- Experience simplifying enterprise reporting, defining common KPIs and enabling governed self-service analytics for senior decision-makers.
- Proven ability to lead agile, cross-functional and globally distributed teams and to manage vendors, budgets, dependencies and delivery risk.
- Executive-level influence and communication: able to frame decisions, challenge assumptions, explain technical trade-offs simply and secure alignment without direct authority.
- Strong commercial judgement, curiosity and learning agility, with a record of anticipating business needs and converting emerging technology into practical, scalable outcomes.
- Track record of coaching leaders and building scarce AI, data, digital and product capabilities.
Desirable experience
- Postgraduate qualification in data science, computer science, engineering, information systems, business or a related discipline.
- Experience with explainable AI, model risk management, MLOps or LLMOps, knowledge graphs, data mesh or fabric concepts, and AI-ready data foundations.
- Experience redesigning operating models and workflows to embed AI at scale, including change, adoption and capability-building.
- Country, regional or global experience in Insights, Analytics, Forecasting, Commercial Excellence or Digital.
- Ability to work globally, including international travel where required.
What success looks like
- Leaders use one trusted, decision-ready view of commercial performance.
- Priority data and AI products have clear owners, standards, controls, adoption measures and realised business value.
- AI-enabled workflows are scaled responsibly beyond pilots, with defined human accountability and evidence of improved quality, speed or productivity.
- Data duplication and manual reporting reduce as reusable products, common definitions and governed self-service increase.
- The team has stronger AI, data architecture, digital product and commercial decision skills, with a sustainable succession pipeline.
Date Posted
22-Sep-2026
Closing Date
05-Oct-2026
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
