Digital Manufacturing Transformation Consultant | Life Sciences

Accenture•St. Louis, MO

5d•$54,400 - $205,800

About The Position

Accenture is seeking a Consultant for manufacturing and operations transformation within its Supply Chain & Engineering practice for Life Sciences, with a focus on data and AI transformation. The role works at the intersection of manufacturing, quality, and supply chain to deliver AI-enabled transformations for pharmaceutical, biotech, medical technology, distributor, and consumer health clients. The consultant produces data-driven analysis, assesses current processes and technology, designs future-state processes, and supports transformation recommendations. The role applies AI and automation to business processes and considers how AI agents and people interact within redesigned processes through Agentic Experience (AX) Design. Travel is required and may vary up to 75%.

Requirements

At least 3 years of experience in process excellence, process redesign, or management consulting.
At least 1 year using AI-assisted analytics, visualization, and generative AI tools for content generation and low-fidelity user interfaces.
At least 1 year integrating AI and automation into business process design.
At least 1 year delivering project work in a consulting environment or industry experience in at least one domain such as Life Sciences, Consumer Goods, Industrial, Energy, Utilities, or Chemical and Natural Resources.
Bachelor’s degree or equivalent, or a minimum of 12 years of work experience. Applicants with an associate degree must have at least 6 years of work experience.
Travel may be required up to 75%.
Applicants for U.S. employment must have work authorization that does not now or in the future require visa sponsorship.

Nice To Haves

Familiarity with process mapping or process modeling tools.
Strong analytical skills and the ability to communicate complex findings in clear business terms and structured recommendations.
Client-facing consulting or advisory experience.
Fluent, independent use of AI tools, including building agents, to improve the quality of deliverables.
Ability to embed AI Design principles into process specifications, including agent behaviors, autonomy levels, and handoffs.
Ability to identify process steps that could be transformed by agentic AI and articulate the design implications.
Experience using AI-assisted analytics, visualization, and benchmarking tools to ground future-state designs.
Demonstrated continuous learning with emerging AI tools.
Expertise in big data technologies, ontology, and AI/ML frameworks.
Ability to analyze data and translate findings into structured recommendations.
Understanding of modern data and AI strategy, including data mesh, data products, semantic layers, and their role in enabling AI-driven manufacturing use cases.
Familiarity with digital manufacturing platforms, factory IT/OT, digital twins, and advanced automation, including manufacturing architecture experience.
Understanding of GxP and regulatory considerations in pharmaceutical manufacturing and how they shape data and AI solutions.

Responsibilities

Use relevant AI tools across tasks and deliverables, including creating low-code proof of concepts, agents, and other artifacts.
Assess clients’ current processes, technology tools, and organizational structures to identify opportunities to move toward data- and AI-driven operations.
Design future-state process specifications for Life Sciences manufacturing and quality, including AI integration points, operating model changes, human-machine interactions, and workforce adoption.
Conduct process analysis and support delivery of digital and platform solutions across operations.
Build analytical frameworks and data-driven assessments using AI-assisted visualization and analysis tools.
Develop change-impact assessments using AI-assisted organizational analysis tools.
Review and improve domain-specific reinvention designs and functional playbook content using industry or functional knowledge.
Contribute to the reference library and maintain domain expertise through continuous learning.
Apply AX Design principles to specify or review AI integration designs, including agent behavior, autonomy, and handoffs to people.
Apply generative AI and agent-based modeling to accelerate scenario planning, exception management, and autonomous decision-making.
Define and implement AI-enabled supply chain control towers for real-time visibility, risk sensing, and proactive disruption management.
Collaborate with clients to deploy predictive maintenance, digital twins, and intelligent automation in manufacturing and distribution networks.
Translate business challenges into AI/ML use cases, build proofs of concept, and scale solutions to the enterprise level.
Support data strategy, governance, and architecture to enable high-quality, AI-ready supply chain data across ERP, PLM, MES, and logistics platforms.
Use Accenture’s AI ecosystem and partnerships, including AWS, Microsoft Azure, Google Cloud, NVIDIA, and data/ML vendors, to deliver solutions.
Develop business cases and value-realization frameworks that quantify AI-driven cost reduction, service improvement, and sustainability impact.
Contribute to practice development and knowledge sharing, and mentor junior consultants in AI and supply chain transformation programs.