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# Digital Manufacturing Data & AI Consultant Life Sciences

- **Company:** Accenture
- **Location:** St. Louis, MO, United States
- **Salary:** $70,350.0 - $205,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Application Integration Architecture, Microsoft Azure, Big Data, Business Process Modeling, Digital Twin, Google Cloud, Data Strategy, Data Layers, Data Analytics, Low-code, GXP
- **Published:** August 25, 2026
- **Apply:** https://www.salesheads.com/job.asp?id=3364632376&tx=UT545TYI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Minimum 3 years of experience in process excellence, process redesign, or management consulting
 * Minimum 1 year using AI-assisted analytics, visualization and Gen AI tools for content generation as well as low-fi user interfaces (UI)
 * Minimum 1 year of integrating AI and automating business process design
 * Minimum 1 year of experience delivering project work in consulting environment OR industry experience in at least one domain (e.g., Life Sciences, Consumer Goods Industrial, Energy, Utilities or Chemical and Natural Resources)
 * Bachelor's degree or equivalent (minimum 12 years) work experience. (If associate degree, must have a minimum of 6 years' work experience)

Bonus Points If:

 * Familiarity with process mapping or process modeling tools
 * Strong analytical skills with the ability to communicate complex findings in clear business terms/structured recommendations
 * Client-facing consulting or advisory experience
 * Applies AI tools fluently and independently, including building agents, for higher-quality deliverables to business problems
 * Embeds AI Design principles into process specifications (agent behaviors, autonomy levels, handoffs)
 * Identifies where agentic AI can transform specific client process steps and articulates design implications
 * Uses 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, AI/ML framework ?
 * 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 how these foundations enable AI-driven manufacturing use cases
 * Familiarity with digital manufacturing platforms, factory IT/OT, digital twins, and advanced automation. Experience in architecting for use cases in Manufacturing
 * Understanding of GxP and regulatory considerations in pharma manufacturing, and how they shape data and AI solutions

## Description

Utilize relevant AI tools across all tasks and deliverable creation, including the creation of low-code proof of concepts (POCs), agents and other artifacts that facilitate:

 * Be able to analyze client's current state by assessing processes, technology tools and organizational structure with an intent to transform to a data and AI insight driven future state.
 * Design future-state process specifications covering AI integration points, operating model changes, human-machine interaction requirements, and workforce adoption approaches applicable to Life Sciences manufacturing and quality functions. .
 * 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.
 * Apply your industry or function knowledge to review and improve domain-specific reinvention designs and functional playbook content.
 * Contribute to the reference library and keep your domain expertise current through continuous learning.
 * Apply AX Design principles when specifying or reviewing AI integration designs - defining how AI agents should behave within each redesigned process step, what they handle autonomously, and when they hand off to a person.

At a Consultant level within Life Sciences Supply Chain and Engineering , key project responsibilities could include:

 * 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 proof-of-concepts, and scale solutions to enterprise level.
 * Support data strategy, governance, and architecture to ensure high-quality, AI-ready supply chain data across ERP, PLM, MES, and logistics platforms.
 * Bring Accenture's AI ecosystem and partnerships (AWS, Microsoft Azure, Google Cloud, NVIDIA, data/ML vendors) to deliver cutting-edge solutions.
 * Deliver business case development and value realization frameworks, quantifying AI-driven cost reduction, service improvement, and sustainability impact.
 * Contribute to practice development, knowledge sharing, and mentoring of junior consultants in AI and supply chain transformation programs.


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