We manufacture and supply reliable, high-quality medicines and vaccines to meet patients' needs and drive our performance.
Our network of 37 medicines and vaccines manufacturing sites delivered 1.7 billion packs of medicines and 409 million vaccine doses in 2024 to help make a positive impact on the health of millions of people. Our supply chain is not just core to our operations; it's vital to bringing our innovations to patients as quickly, efficiently and effectively as possible. Technology is transforming how we manufacture medicines and vaccines, enabling us to increase the speed, quality and scale of product supply.
We need the very best minds and capability to help us on our journey to make more complex products, harnessing the power of smart manufacturing technologies including robotics, digital solutions and artificial intelligence to deliver for patients.
Position Summary
The Smart Manufacturing Execution Lead is a global role accountable for executing the Smart Manufacturing strategy across the GSC network, accelerating 'data to value' through Industry 4.0 capabilities and advanced analytics/AI.
The role will support identified GSC manufacturing sites and functions as prioritized by SM wave delivery and business value impact.
The role partners with GPS, Manufacturing, Quality, NPI/CMC, Supply Chain, Engineering/Automation, Digital/Tech, Enterprise AI and external partners to identify opportunities, prioritize value, and deliver scalable solutions that deliver business impact and simplify operations across our global supply chain.
The role will help to accelerate delivery of the Smart Manufacturing vision of a connected, predictive, agile supply chain, leveraging technology, data and AI to transform work, maximize productivity, enhance performance and accelerate medicines and vaccines to patients
Responsibilities
Execute the Smart Manufacturing strategy and translate the Smart Manufacturing vision into an accelerated outcomebased multiyear roadmap, aligned to GSC objectives and governance.
Lead value identification and prioritization at key sites: assess digital maturity, diagnose performance opportunities, quantify benefit ranges, and select projects using a valuedriven approach.
Deliver high impact Industry 4.0 solutions endtoend (e.g. IT/OT integration, manufacturing data intelligence, connected worker solutions, MES/eBR, SCADA/DCS integration, process analytical technology, advanced analytics, lab automation, model predictive control and AI) in partnership with global and site teams.
Accelerate 'data to value' by improving data availability, @contextualization and consumption models so that data is immediately usable from multiple sources for decisionmaking, monitoring, optimization and advanced control.
Build and lead crossfunctional delivery matrix teams (Ops, Quality, MSAT, Supply Chain, Engineering, Automation, Digital/Tech) to design, pilot, scale and embed solutions into standard ways of working.
Define and maintain global standards and playbooks for solution design, validation, data integrity, cybersecurity, and supplier delivery to ensure compliant, repeatable deployments and in alignment with global stage gate process expectations.
Own stakeholder engagement and influence: partner with senior leaders and frontline teams to align priorities, remove barriers, enable change and drive adoption; role model enterprise mindset, collaborative leadership and GSK behaviors.
Ensure benefits realization: establish KPIs, track value delivery (e.g., productivity, OEE, yield, lead time, deviations, cost of poor quality), and provide clear reporting and insights to governance forums.
Develop capability and community: build digital/data fluency through training, coaching and knowledge transfer; strengthen the network's ability to sustain and extend solutions.
Manage external partners/suppliers to deliver highquality outcomes on time and within agreed scope, ensuring interoperability with the broader technology landscape.
Why You?
Basic Qualification
Bachelor's degree (or equivalent experience) in Science, Engineering, Advanced Technology, Automation, Data Science, AI, or a related discipline.
6 years + demonstrated hands on experience delivering complex, crossfunctional transformation initiatives that drive value focused innovation and change in a regulated and complex manufacturing environments.
Experience with full life cycle of projects from business case creation to hyper care/BAU.
Strong manufacturing operational knowledge (manufacturing operations, process control, MES/eBR, OT/SCADA/DCS, cloud and edge) and ability to work across Quality, Technical/Engineering and Supply Chain interfaces.
Proven capability in application of advanced analytics, data science, AI to manufacturing and/or supply chain problems (e.g., anomaly detection, predictive monitoring, simulation, optimization, decision support, advanced control).
Experience leading multisite / global programs with external supplier reliance, partner delivery and matrix leadership.
Background experience in continuous improvement, six sigma and/or lean manufacturing initiatives
Preferred Qualification
Strong stakeholder management and influencing skills at all levels; able to communicate complex technical topics in business terms.
Value realization and financial acumen: ability to build business cases, define KPIs, and track benefits to outcomes.
Understanding of data integrity, validation expectations (GxP), ISA-95/ISA-88, cybersecurity frameworks, and compliant digital delivery.
Knowledge of Quality, NPI/CMC and Supply Chain processes and the data/decision flows between them.
Familiarity with MLOps practices and the challenges of deploying and governing ML models in regulated manufacturing environments.
Experience of primary and secondary manufacturing in small and/or large molecule facilities
Excellent communication skills
Strong stakeholder management and influencing skills at all levels; able to communicate complex technical topics in business terms.
Value realization and financial acumen: ability to build business cases, define KPIs, and track benefits to outcomes.
Understanding of data integrity, validation expectations (GxP), ISA-95/ISA-88, cybersecurity frameworks, and compliant digital delivery.
Knowledge of Quality, NPI/CMC and Supply Chain processes and the data/decision flows between them.
Familiarity with MLOps practices and the challenges of deploying and governing ML models in regulated manufacturing environments.
Experience of primary and secondary manufacturing in small and/or large molecule facilities
Excellent communication skills
