Smart Manufacturing Execution Lead
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 supports identified GSC manufacturing sites and functions as prioritized by SM wave delivery and business value impact.
Responsibilities
Execute the Smart Manufacturing strategy and translate the Smart Manufacturing vision into an accelerated outcome-based multi-year 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 value-driven approach.
Deliver high impact Industry 4.0 solutions end-to-end (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 decision-making, monitoring, optimization and advanced control.
Build and lead cross-functional 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 front-line 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 high-quality outcomes on time and within agreed scope, ensuring interoperability with the broader technology landscape.
Why You?
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, cross-functional 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).
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 multi-site/global programs with external supplier reliance, partner delivery and matrix leadership.
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.