Field of studyBusiness, Computer Science, Engineering, Information Systems, Operations, Supply Chain (or related)
Study levelBachelor's
Original posting
Location: Anywhere in Country
At EY, we’re all in to shape your future with confidence.
We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.
The opportunity
As a Manager in our Supply Chain Manufacturing practice, you will lead market-facing delivery of manufacturing transformation programs that combine operational improvement, digital technology, advanced analytics, and AI. You will work directly with client executives, plant leaders, operators, engineers, quality, maintenance, supply chain, IT, OT, data, and AI teams to turn business priorities into implementable solutions that improve productivity, reliability, quality, visibility, decision-making, and manufacturing agility. You will help clients establish the trusted data and knowledge foundations required to scale predictive, generative, and agentic AI across plant and network operations.
Your key responsibilities
As a Manager in Supply Chain Manufacturing, you will be responsible for driving digital and AI-enabled solutions, plant transformation, and the application of frameworks essential to our clients’ manufacturing goals.
Lead client-facing manufacturing transformation workstreams and programs from opportunity shaping through design, implementation, deployment, and value realization.
Act as the two-way liaison between design and delivery teams by translating client needs into solution requirements and translating reusable capabilities into practical engagement plans.
Partner with the design the team prioritize solution enhancements, validate use cases, shape demonstrations, assess implementation readiness, and define the documentation, training, and support needed for scalable delivery.
Capture lessons, recurring requirements, configuration patterns, technical constraints, and delivery feedback from engagements and incorporate them into the design backlog and solution roadmap.
Assess manufacturing processes, performance gaps, user needs, data flows, controls, and technology constraints across plant and network environments.
Translate manufacturing priorities into process designs, functional requirements, user stories, data requirements, integration requirements, acceptance criteria, deployment roadmaps, and measurable outcomes.
Guide solution design across ERP, MES/MOM, connected worker, SCADA, historians, LIMS, QMS, EAM/CMMS, WMS, APS, industrial data platforms, knowledge platforms, AI services, analytics, and reporting environments, based on engagement needs.
Lead functional design, configuration oversight, prototyping, testing, validation, cutover, training, change adoption, hypercare, and benefits tracking.
Lead and coach multidisciplinary delivery teams, review work products, and establish clear accountability for outcomes.
Support technical sales through solution shaping, demonstrations, estimates, proposals, implementation approaches, and responses to requests for proposal.
Identify follow-on opportunities based on client outcomes and emerging manufacturing priorities while maintaining trusted client relationships.
Shape and deliver manufacturing AI use cases such as predictive maintenance, quality intelligence, root-cause analysis, process optimization, intelligent scheduling, energy optimization, knowledge assistants, copilots, and AI-enabled frontline workflows.
Design manufacturing data and knowledge foundations that connect structured, unstructured, time-series, event, image, document, and engineering data across plant, edge, and cloud environments.
Lead the definition of manufacturing ontologies, common data models, semantic models, and semantic layers covering assets, equipment hierarchies, materials, products, orders, batches, recipes, processes, quality events, maintenance records, people, locations, and performance measures.
Guide the implementation of knowledge graphs that connect operational entities, relationships, events, documents, and business rules to support contextual search, multi-hop reasoning, traceability, explainability, and reusable AI services.
Define and implement retrieval-augmented generation approaches, including document RAG, hybrid retrieval, and graph-augmented RAG, using vector search, metadata, semantic relationships, and governed source content to ground AI responses.
Translate manufacturing knowledge into machine-readable structures, retrieval strategies, prompts, agent instructions, decision rules, and reusable context services that improve AI relevance and reduce unsupported outputs.
Establish AI delivery and governance requirements covering data quality, lineage, provenance, access controls, model evaluation, human oversight, cybersecurity, intellectual property, regulatory compliance, monitoring, and responsible AI.
Plan and execute AI pilots from use-case prioritization and value framing through data readiness, prototyping, evaluation, deployment, adoption, benefits tracking, and scaling across sites.
Skills and attributes for success
To excel in this role, you will need a blend of technical and business skills, including relationship management, commercial acumen, and communication. You should be adept at complex problem-solving and critical thinking, with a strong capacity for change management.
Manufacturing functional leadership: Strong understanding of production operations, planning and scheduling, shop-floor execution, operational excellence, asset productivity, maintenance, quality, warehouse operations, material flow, performance management, or new plant and line start-up.
Digital manufacturing technology: Implementation or delivery experience with manufacturing platforms such as MES/MOM, connected worker, electronic work instructions, EBR, digital performance management, maintenance, quality, warehouse, planning, industrial data, or analytics solutions.
Delivery translation: Ability to convert repeatable solution assets into client-specific delivery approaches and convert delivery feedback into clear requirements, priorities, reusable patterns, and roadmap recommendations for solutions.
Architecture and systems landscape: Working knowledge of how ERP and supply chain applications connect with MES/MOM, SCADA, historians, PLC-enabled data, LIMS, QMS, EAM/CMMS, WMS, APS, cloud, edge, integration, and reporting layers.
Requirements, data, and integration: Ability to lead process design, functional requirements, user stories, workflows, data mapping, interface requirements, master-data considerations, controls, roles, reporting, and acceptance criteria.
Implementation leadership: Experience leading design, configuration oversight, testing, validation, deployment, cutover, training, adoption, hypercare, value tracking, and continuous improvement.
Client and team leadership: Ability to facilitate executive and plant-level discussions, manage multidisciplinary teams, communicate complex topics clearly, coach colleagues, and build trusted relationships.
Commercial and delivery management: Experience managing scope, resources, economics, quality, risk, proposals, estimates, technical sales, and opportunities for extended services.
AI-enabled manufacturing: Ability to connect predictive, generative, and agentic AI capabilities with practical manufacturing use cases, operating workflows, controls, and measurable business outcomes.
Manufacturing knowledge engineering: Experience defining ontologies, taxonomies, entity relationships, business rules, and common manufacturing data models that create shared meaning across operational and enterprise systems.
Semantic architecture: Ability to design governed semantic layers that harmonize definitions, hierarchies, measures, lineage, and context for analytics, digital twins, AI applications, and cross-site reuse.
Knowledge graphs: Working knowledge of graph-based modeling and retrieval approaches that connect assets, products, processes, materials, orders, quality, maintenance, documents, events, and people.
RAG and AI grounding: Understanding of retrieval-augmented generation, vector databases, embeddings, hybrid search, graph-augmented retrieval, chunking, metadata design, source attribution, evaluation, and techniques for improving accuracy and explainability.
AI architecture and delivery: Working knowledge of LLMs, machine learning, AI agents, orchestration, APIs, cloud and edge inference, MLOps/LLMOps, model monitoring, and integration with manufacturing systems and workflows.
Responsible AI and governance: Ability to define practical controls for security, privacy, data access, provenance, validation, human oversight, regulatory compliance, model risk, and ongoing performance management.
To qualify for the role, you must have
A bachelor's degree in engineering, supply chain, operations, information systems, computer science, business, or a related field.
At least 4 to 6 years of relevant experience in digital manufacturing, manufacturing operations, supply chain, industrial technology, systems implementation, consulting, or a related environment.
Experience shaping or delivering AI, advanced analytics, or knowledge-enabled solutions within manufacturing, supply chain, engineering, quality, maintenance, or industrial operations.
Experience working with manufacturing data models, semantic models, ontologies, knowledge graphs, RAG solutions, or comparable approaches for contextualizing and governing operational data.
Experience translating AI use cases into data, knowledge, architecture, integration, security, evaluation, adoption, and value-realization requirements.
Experience leading client-facing workstreams or projects that connect manufacturing processes with technology implementation.
Experience translating between functional stakeholders and technical teams, with ownership of delivery artifacts such as process maps, requirements, functional designs, data mappings, interface requirements, test strategies, deployment plans, or value cases.
Ability and willingness to travel to client and manufacturing sites as required by engagement needs. Travel can range from 80-100%.
Strong written and verbal communication skills, with the ability to influence business, engineering, IT, OT, vendor, and leadership stakeholders.
Ideally, you’ll also have
Experience in life sciences, consumer products, chemicals, automotive, aerospace and defense, industrial products, or advanced manufacturing.
Experience with manufacturing operating models, network optimization, Lean, Six Sigma, TPM, IWS, reliability, cost optimization, contract manufacturing, or vertical start-up.
Experience with manufacturing standards and reference models such as ISA-88, ISA-95, OPC UA information models, asset administration shells, or comparable industrial ontologies and data-modeling standards.
Working knowledge of IT/OT convergence, industrial cybersecurity considerations, cloud or edge patterns, and shop-floor connectivity.
Hands-on exposure to graph databases, vector databases, semantic technologies, metadata platforms, industrial data fabrics, unified namespace architectures, digital twins, or AI development platforms.
Experience designing or implementing RAG, GraphRAG, manufacturing copilots, intelligent agents, natural-language interfaces, or AI-enabled decision-support workflows.
Experience establishing AI evaluation frameworks, governance controls, reusable prompt and retrieval patterns, knowledge-curation processes, or MLOps/LLMOps capabilities.
Familiarity with computer vision, time-series machine learning, anomaly detection, optimization, simulation, predictive maintenance, quality analytics, or edge AI.
Experience in regulated manufacturing, computer system validation, data integrity, or quality and compliance requirements.
Relevant platform, project management, Agile, Lean, Six Sigma, or manufacturing certifications.
What we look for
We seek market-facing leaders who can move confidently between the client, the plant floor, the data and AI architecture, and the delivery team. The strongest candidates combine manufacturing credibility with technology and AI fluency, understand that scalable AI depends on trusted data, shared semantics, and governed knowledge, and can turn ambiguous operational needs into implementable solutions. They know when to apply traditional analytics, machine learning, generative AI, knowledge graphs, or agentic approaches—and when not to. They create a practical feedback loop between the market and solution development so that each engagement delivers measurable client value, strengthens reusable data and knowledge assets, and improves the repeatability of future AI-enabled delivery.
What we offer you
At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.
The salary range for this job is:
New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $154,000 to $256,700
Bay Area California offices – $160,500 to $267,400
All other offices locations in the US, including Sacramento – $128,400 to $235,300
Individual salaries within these ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
Are you ready to shape your future with confidence? Apply today.
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EY accepts applications for this position on an on-going basis.
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EY | Building a better working world
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All in to shape the future with confidence.
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