Business Performance -Supply Chain Manufacturing Ops & AI Transformation Delivery - Senior Manager

EY•Hartford, DC

6d•$150,400 - $390,500•Remote

About The Position

Client-facing manufacturing and AI transformation leader accountable for shaping, mobilizing, and delivering complex, technology-enabled solutions that combine deep manufacturing domain expertise with modern data, AI, and industrial technology capabilities to produce measurable operational and business outcomes. As a Senior Manager in Supply Chain Manufacturing, you will lead client-facing transformation programs across manufacturing strategy, operational excellence, plant performance, digital manufacturing, industrial AI, data platforms, and technology-enabled operations. You will guide clients from problem definition and value-case development through functional and technical design, implementation, adoption, stabilization, scaling, and value realization. This delivery role requires a strong techno-functional balance: the credibility to lead manufacturing operating-model and process transformation, together with the technology fluency to direct modern data, AI, application, integration, and IT/OT delivery. You will lead multidisciplinary teams and remain accountable for quality, risk, outcomes, and the client experience.

Requirements

A bachelor's degree in engineering, supply chain, operations, information systems, business, or a related discipline; a master's degree is preferred.
No less than 5–7 years of relevant experience spanning manufacturing, supply chain, operational excellence, digital manufacturing, industrial data and AI, technology consulting, or a related field.
Experience leading complex manufacturing transformation, solution development, or technology-enabled improvement initiatives.
Experience leading Managers and multidisciplinary teams across business, functional, data, and technology workstreams.
Practical knowledge of manufacturing strategy and operating models, P&G’s Integrated Work System (IWS) or comparable operational excellence systems, plant transformation, manufacturing technology landscapes, and emerging AI capabilities.
Ability to travel as needed based on business and role requirements.
Experience delivering client-facing manufacturing transformation programs through design, implementation, deployment, and stabilization.
Experience managing program governance, scope, schedule, resources, quality, financial performance, risks, issues, and dependencies.
Experience leading senior client stakeholders and multidisciplinary delivery teams.
Experience translating manufacturing business requirements into process, data, AI, functional, integration, security, and technical architecture requirements, with traceability through build, test, deployment, and value realization.
Deep manufacturing and supply chain expertise with the ability to connect operating-model, process, data, technology, and workforce considerations.
Strong techno-functional leadership: able to engage credibly with operators, engineers, and manufacturing leaders while translating their needs for architects, data engineers, data scientists, developers, cybersecurity teams, and platform owners.
Knowledge of modern industrial data architecture, including lakehouse and data-mesh patterns, time-series and event data, semantic layers, ontologies, canonical data models, knowledge graphs, data products, APIs, streaming integration, MLOps, and DataOps.
Ability to evaluate AI opportunities for feasibility, value, data readiness, workflow fit, adoption, scalability, and risk—and to move priority use cases from prototype through production and enterprise deployment.
Executive communication, structured problem-solving, systems thinking, commercial awareness, and the ability to influence senior stakeholders.
Ability to lead multidisciplinary teams, manage competing priorities, and maintain high standards for quality and risk management.
Knowledge of manufacturing domains such as production performance, asset productivity, maintenance, quality, planning, warehousing, operational excellence, and IT/OT integration.
Demonstrated ability to mobilize and recover complex programs, make decisions under ambiguity, and drive teams toward outcomes.
Strong client relationship, facilitation, negotiation, change leadership, and conflict-resolution skills.
Commercial delivery acumen covering scope, estimates, staffing, engagement economics, contracts, change control, and value realization.

Nice To Haves

Experience leading multi-site or global manufacturing transformation and deployment programs.
Knowledge of ISA-95, Purdue models, industrial data architectures, IT/OT integration, and manufacturing cybersecurity considerations.
Implementation experience with SymphonyAI industrial solutions or comparable industrial AI platforms, as well as MES/MOM, connected worker, industrial analytics, digital performance management, asset performance management, quality, maintenance, LIMS, ERP, or warehouse solutions.
Experience with agile, hybrid, and traditional program-delivery methods, including testing, cutover, adoption, and hypercare.
Experience applying AI agents, digital assistants, predictive analytics, anomaly detection, root cause intelligence, and autonomous decision support to manufacturing processes and operational challenges.
Experience designing or delivering industrial data platforms that connect IT, OT, engineering, and edge sources using unified namespaces, semantic and graph-based models, ontologies, knowledge graphs, asset and performance models, APIs, metadata, lineage, and data governance.
Relevant program management, operational excellence, cloud, data, or manufacturing technology certifications.

Responsibilities

Lead the end-to-end delivery of complex manufacturing transformation programs, from mobilization and design through implementation, deployment, stabilization, and value realization.
Own senior client relationships, establish delivery governance, align stakeholders, facilitate decisions, and communicate program status, risks, dependencies, and outcomes.
Translate manufacturing priorities and P&L value levers into executable workplans, functional and technical solution designs, process changes, AI use cases, technology requirements, data products, operating-model changes, and adoption plans.
Direct multidisciplinary teams across manufacturing operations, functional design, product management, data engineering, data science, AI, enterprise and solution architecture, application delivery, integration, change management, testing, deployment, and support.
Manage scope, schedule, quality, budget, resources, dependencies, issues, risks, and contractual commitments using disciplined delivery and risk-management practices.
Lead manufacturing process and solution design across production performance, asset productivity, maintenance, quality, planning, warehousing, operational excellence, and IT/OT integration.
Lead the design and delivery of scalable industrial data foundations, including cloud and edge data platforms, unified namespaces, canonical and semantic data models, ontologies, asset hierarchies, master data, knowledge graphs, metadata, lineage, and governance.
Shape and industrialize AI solutions—including predictive and prescriptive analytics, generative AI, copilots, agents, anomaly detection, root-cause intelligence, digital twins, and decision support—while establishing appropriate model governance, security, monitoring, human oversight, and responsible AI controls.
Connect technology delivery to manufacturing management systems and operational excellence, including P&G’s Integrated Work System (IWS), zero-loss methods, total employee ownership, daily direction setting, standards, capability building, and sustained performance improvement.
Guide solution configuration, integration, testing, deployment, cutover, training, adoption, and operational handover while ensuring business and technical readiness.
Define outcome measures, establish performance baselines, track value realization, and work with client leaders to sustain improvements.
Identify follow-on opportunities based on client needs and delivery insights and collaborate with account teams to shape extensions and additional services.
Capture delivery lessons, reusable assets, and implementation insights for incorporation into the broader manufacturing knowledge and solution portfolio.
Lead, coach, and develop Managers and delivery teams while fostering accountability, collaboration, inclusion, and high performance.