Digital Manufacturing Data & AI Consultant | Life Sciences

Accenture•St. Louis, MO

10d•$54,400 - $205,800•Hybrid

About The Position

Accenture is seeking a Consultant for Manufacturing and Operations transformation work within our Supply Chain & Engineering practice for Life Sciences with a focus on Data and AI transformation. We work with our pharmaceutical, biotech, medical technology, distributor and consumer health clients globally to redefine the future of the life sciences industry: combining the latest technology with scientific breakthroughs to revolutionize how medical treatments are discovered, developed and delivered to patients around the world. The role sits at the intersection of manufacturing, quality and supply chain, and is responsible for delivering AI-enabled transformations for our clients and their patients.

Requirements

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)

Nice To Haves

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

Responsibilities

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.
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.