Business Performance - Supply Chain Manufacturing Operations Solution - Senior Manager - Consulting

EY•Hartford, DC

23d•$150,400 - $390,500

About The Position

EY is hiring a Senior Manager in Supply Chain Manufacturing Operations to lead the design, development, testing, and scaling of differentiated AI-enabled manufacturing solutions. The role combines manufacturing and supply chain expertise with industrial data platforms, data architectures, predictive and generative AI, knowledge graphs, RAG, ontologies, and reusable application components. It is a senior, hands-on solution leadership position focused primarily on creating working solutions, prototypes, accelerators, demonstrations, reference architectures, and reusable intellectual property for EY pursuit and delivery teams to configure and deploy. The Senior Manager sets solution strategy and portfolio direction, leads Managers and multidisciplinary teams, and is accountable for technical credibility, quality, risk management, commercial relevance, talent development, repeatability, and adoption. The role directs manufacturing data foundations and AI solutions, governs development and release practices, manages roadmaps and investment, supports business development, and packages solutions for reuse. It interacts selectively with internal stakeholders, alliance partners, pursuit and delivery teams, and clients; its primary accountability is internal solution engineering rather than ongoing client engagement delivery.

Requirements

Bachelor’s degree in engineering, computer science, data science, information systems, manufacturing, supply chain, operations, or a related discipline.
At least 8–10 years of relevant experience in AI solution development, industrial data platforms, digital manufacturing, manufacturing technology, supply chain consulting, data engineering, software development, or a related field.
Demonstrated experience building working AI, data, analytics, or software solutions, not solely defining strategy, managing programs, or delivering advisory services.
Experience developing manufacturing data platforms, industrial data pipelines, common data models, semantic layers, ontologies, or knowledge graphs.
Experience designing or implementing RAG-based applications, AI copilots, intelligent search, conversational interfaces, or agentic workflows.
Experience with data modeling across operational, manufacturing, engineering, maintenance, quality, or supply chain domains.
Experience taking manufacturing use cases from problem definition through architecture, build, configuration, testing, demonstration, and reusable solution packaging.
Experience integrating structured, unstructured, time-series, event, image, and document data from manufacturing and enterprise systems.
Experience using programming, scripting, low-code, no-code, data-engineering, AI-development, or application-development tools to produce working solutions.
Experience creating technical documentation, reference architectures, data-model specifications, test plans, demonstrations, and deployment guidance.
Demonstrated experience leading Managers and multidisciplinary technical teams through iterative solution-development cycles, including work planning, delegation, review, feedback, performance management, and capability building.
Experience influencing senior stakeholders, supporting pursuits and commercial decisions, managing solution-development risks and investments, and operating effectively in a primarily internal solution-building role with selective client interaction.
Ability to travel up to 50%.
Deep manufacturing and supply chain credibility, connecting operating model, process, data, technology, workforce, and business value.
Senior-level solution-engineering leadership across manufacturing operations, industrial data, AI, applications, integration, cybersecurity, and deployment.
Strong understanding of manufacturing data platforms, industrial DataOps, unified namespaces, data fabrics, data products, contextualization, semantic modeling, ontologies, knowledge graphs, and edge-to-cloud architectures.
Practical experience directing RAG or GraphRAG solutions, including ingestion, chunking, embeddings, vector and graph retrieval, reranking, grounding, prompting, evaluation, observability, and responsible AI controls.
Strong data-modeling and architecture skills across conceptual, logical, physical, semantic, time-series, event, graph, and application models, including alignment with ISA-95, ISA-88, asset hierarchies, and manufacturing process models.
Ability to evaluate predictive, prescriptive, generative, and agentic AI opportunities for feasibility, value, data readiness, workflow fit, adoption, scalability, and risk.
Working knowledge of MES/MOM, historians, SCADA, PLC, CMMS/EAM, LIMS, QMS, ERP, warehouse, planning, engineering, connected-worker, cloud, database, streaming, API, and industrial protocol technologies.
Strong product-management and agile-development capabilities, including portfolio strategy, roadmap and backlog management, investment prioritization, feature definition, release planning, and iterative prototyping.
Ability to lead Managers and multidisciplinary teams, manage competing priorities, and maintain high standards for technical quality, usability, security, risk, and manufacturing outcomes.
Executive communication, structured problem-solving, systems thinking, facilitation, negotiation, and ability to influence senior business and technical stakeholders.
Commercial awareness and experience supporting business development, proposals, estimates, staffing models, financial planning, alliance relationships, and solution investment decisions.
Commitment to coaching, inclusive leadership, talent development, knowledge sharing, and building high-performing teams.

Nice To Haves

Hands-on experience with SymphonyAI Industrial, IRIS Foundry, IRIS Forge, IRIS Flows, industrial copilots, industrial knowledge graphs, or related SymphonyAI capabilities.
Experience with comparable industrial data and AI platforms such as Cognite Data Fusion, Palantir Foundry, Databricks, Microsoft Fabric and Azure AI, AWS industrial and AI services, Google Cloud data and AI services, Snowflake, AVEVA, AspenTech, Siemens, or PTC.
Experience configuring unified namespaces, asset hierarchies, industrial knowledge graphs, governed data catalogs, low-code applications, AI agents, and persona-based copilots.
Experience with graph technologies such as Neo4j, RDF, OWL, SPARQL, property graphs, ontology-management tools, or graph-based retrieval.
Experience with vector databases, embedding models, LLM frameworks, agent frameworks, model gateways, prompt-management tools, and AI evaluation platforms.
Experience building manufacturing AI use cases in predictive maintenance, asset performance, process optimization, quality, vision inspection, production intelligence, connected worker, energy, scheduling, or supply chain.
Knowledge of industrial and manufacturing standards such as ISA-95, ISA-88, OPC UA, MQTT, Sparkplug, IEC 62264, CFIHOS, or related reference models.
Experience with Python, SQL, APIs, JSON, graph query languages, data pipelines, stream processing, cloud services, containers, or application-development frameworks.
Experience applying responsible AI, cybersecurity, access control, data privacy, model governance, content traceability, and human-approval patterns in industrial environments.
Experience with product management, agile development, design thinking, user-centered design, or solution incubation.
Relevant cloud, AI, data, graph, manufacturing, or platform certifications.

Responsibilities

Set the vision, roadmap, investment priorities, and quality standards for AI-enabled manufacturing solutions and reusable components.
Lead Managers and multidisciplinary teams through solution definition, architecture, build, testing, release, adoption, and continuous improvement; provide hands-on technical direction and resolve critical issues.
Translate manufacturing and supply chain needs into AI use cases, value hypotheses, user stories, functional and technical requirements, data and model requirements, acceptance criteria, and operational outcomes.
Own solution governance, including architecture decisions, responsible AI, cybersecurity, data privacy, quality, risk, release readiness, documentation, and compliance with firm development standards.
Direct the design of manufacturing data platforms that connect and contextualize data from manufacturing and enterprise systems.
Guide reusable manufacturing data models, ontologies, semantic layers, taxonomies, metadata, entity relationships, knowledge graphs, and governance standards.
Lead the design and industrialization of RAG and GraphRAG solutions using governed content and operational data, retrieval methods, evaluation, guardrails, and human oversight.
Shape AI assistants, copilots, agents, predictive models, and intelligent workflows for manufacturing use cases such as predictive maintenance, quality investigation, production and energy optimization, troubleshooting, and operational decision support.
Lead configuration and extension of SymphonyAI industrial capabilities and comparable industrial data and AI platforms.
Oversee reusable integrations and connectors using APIs, event streams, industrial protocols, data pipelines, orchestration tools, and IT/OT integration patterns.
Establish engineering standards for reusable code, source control, configuration and model versioning, data quality, testing, DevOps, DataOps, MLOps, LLMOps, security, release management, and documentation.
Own solution backlogs and product roadmaps; prioritize features, plan releases, manage dependencies, allocate resources, and coordinate agile development.
Partner with practice, account, pursuit, alliance, and delivery leaders to identify market needs, shape offerings, estimate effort and investment, support proposals and demonstrations, and enable adoption.
Support technical sales and business development through discovery, technical qualification, architecture and implementation planning, demonstrations, proofs of concept, proposals, statements of work, estimates, pricing inputs, and presentations.
Contribute to revenue generation by identifying opportunities, shaping value propositions, supporting proposal development and pricing, and building trusted relationships with stakeholders.
Manage solution-development budgets, staffing, milestones, risks, dependencies, and investment decisions; communicate progress and escalations to senior stakeholders.
Package solutions for reuse through reference implementations, technical documentation, configuration guides, architecture diagrams, data-model specifications, test assets, deployment guidance, and enablement materials.
Serve as a senior solution expert for pursuit and delivery teams while remaining primarily accountable for internal solution engineering.
Lead, coach, and develop Managers, engineers, analysts, and specialists; provide feedback, support career development, promote inclusive teaming, and develop future solution leaders.
Assess solution quality through architecture reviews, functional testing, model and retrieval evaluation, performance and security review, and user validation.