Director, Master Data Management

Job description

Position Summary: You will lead and transform GSK’s Master Data Management (MDM) capability for the US organization — owning the vision, architecture, and operating model that deliver a single, trusted view of our most critical master data. Customer mastering (spanning Healthcare Professionals, Accounts, and Patients) is at the heart of this role, and Product mastering will be an equally essential part of the mandate, with the capability designed to extend to additional domains over time. This is a transformation role. You will evolve an established capability — including a team of Data Stewards and business partners — into a modern function that is significantly faster, more automated, and tightly aligned to the speed and agility the business demands. You will bring industry-leading best practices to how data is mastered, governed, and served, and ensure the MDM capability is prioritized around the organization’s most important business needs. A central goal is to make data stewardship dramatically quicker and more responsive, ensuring the MDM function keeps pace with commercial needs. Thoughtful adoption of AI and automation is a key enabler of this transformation, alongside strong governance and disciplined execution. The role requires deep MDM expertise, hands-on experience with the Reltio platform, a strong understanding of healthcare/pharma customer data and commercial data ecosystems, and an AI-driven mindset to modernize how GSK manages, matches, governs, and serves its master data. You will partner closely with the Global Tech organization that operates core MDM technical processes, as well as with Commercial, Market Access, Field Force, Insights & Analytics, and Data Governance teams. Responsibilities: MDM Strategy, Capability & TransformationDefine and lead the strategy, architecture, and multi-year roadmap for GSK’s US MDM capability, establishing trusted golden records across the Customer domain (HCP, Account, Patient) and the Product domain, with a design that scales to additional domains in the future. Lead the ongoing modernization of the MDM capability — evolving platforms, processes, and ways of working to raise data quality, accelerate mastering, and reduce manual effort. Own the end-to-end master data operating model — acquisition, ingestion, matching, governance, quality, and retirement — ensuring data is managed efficiently and compliantly. Define measurable data quality metrics, minimal required attributes, matching and merging rules, and survivorship criteria across each domain to support a robust and successful MDM platform. Partner with a range of key stakeholders to establish a clear gap analysis, evaluating the “present state” against the “target state” and prioritizing the capabilities that unlock the most business value. Ensure the MDM capability is continuously aligned to the organization’s most important business needs, prioritizing the domains, data, and improvements that create the greatest commercial and patient impact. Bring and embed industry-leading MDM best practices — in data quality, governance, stewardship, and architecture — and set the standard for a truly best-in-class capability. Transforming Data Stewardship for Speed & AgilityRe-imagine the data stewardship operating model to make it significantly faster, with an explicit goal that the MDM function keeps pace with the business’s needs for speed and agility. Evolve and develop the existing team of Data Stewards and business partners, redefining roles, workflows, and skills for a modern, AI-augmented operating model. Deploy AI and GenAI to automate high-volume, repetitive stewardship tasks — entity resolution, matching, deduplication, and quality remediation — shifting steward capacity toward higher-value exception handling and governance. Establish and monitor clear KPIs and SLAs for stewardship throughput and turnaround and build the service organization and governance needed to sustain them in day-to-day operations. Critical Data SourcesOwn the strategy for the critical internal and third-party data sources that form the backbone of the MDM capability, ensuring coverage, currency, and quality across all feeds. Manage source onboarding, profiling, validation, and reconciliation processes, and oversee vendor relationships, service levels, and data quality commitments. Continuously rationalize and evolve the data source portfolio, identifying gaps, redundancy, and emerging sources that improve completeness, timeliness, or match rates. Affiliations (B2B & P2B)Own the data models, processes, and tooling for managing affiliations — both business-to-business (B2B) and person/prescriber-to-business (P2B). Ensure affiliation data is accurate, current, and fit for purpose in downstream commercial systems, including CRM, targeting, call planning, market access, and analytics. Explore AI-assisted approaches to infer, propose, and validate affiliations from disparate data signals, reducing dependence on manual curation while keeping humans in the loop for critical decisions. Request Intake & ResolutionOwn and modernize the end-to-end process for intake and resolution of requests from across the organization — including large field-based teams — to update or refine customer information and affiliations. Replace manual, reactive intake with intelligent, streamlined processes — leveraging AI for request classification, triage, and routing, and enabling self-service where appropriate — to reduce time-to-resolution and improve the field experience. Explore tiered service-level frameworks reflecting business urgency and downstream impact and embed request capabilities within the workflows the field already uses. Use request analytics to close the loop with upstream data sources, turning recurring request patterns into proactive data quality improvements rather than repeated manual fixes. Innovation, AI & Continuous ImprovementServe as the primary driver of AI adoption within the MDM function, identifying, piloting, and scaling use cases that materially improve data quality, operational speed, and stakeholder experience. Evaluate and adopt agentic and automation capabilities to enable a more “hands-off” stewardship model for routine tasks, with clear human-in-the-loop checkpoints and model monitoring. Maintain a transparent, well-governed approach to AI, documenting automated decision logic and ensuring MDM data quality standards keep pace with GSK’s expanding use of AI. Actively monitor MDM trends, tools, and best practices both within the pharmaceutical industry and beyond, staying informed of emerging approaches and identifying opportunities to further increase speed, agility, and data quality. Delivery, Governance & People LeadershipWork closely with the Global Tech organization that owns and operates core MDM technical processes and rules, providing critical technical direction and requirements. Collaborate with Architecture and Data Governance forums for technical reviews and approvals through the delivery lifecycle. Drive delivery of MDM programs to agreed time, cost, and quality parameters, incorporating agile ways of working to demonstrate value periodically. Define and monitor data compliance policies for customer and product data, understanding relevant global healthcare/pharma regulations and ensuring adherence. Ensure risks and issues across owned programs are recorded, managed, and mitigated in a timely manner with clear owners and timelines. Lead, evolve, and grow a high-performing team, including oversight of outsourced or agency support, potentially across globally located reports. Why You? Basic Qualifications:We are seeking professionals with the following required skills and qualifications to help us achieve our goals: Bachelor’s degree in computer science, Information Systems, Data Science, or a related field, or an equivalent combination of education and experience. 10 or more years’ experience in data management, master data management, or data