Principal Value Engineer - Consumer Packaged Goods (cpg) Industry

Celonis Celonis · Data AI · New York, NY · Value Engineering

This role focuses on bridging technical process mining and AI-driven automation with strategic business outcomes within the Consumer Packaged Goods (CPG) industry. The Principal Value Engineer will act as an architect for SaaS adoption, translating operational data and predictive insights into clear value drivers, maximizing Time-to-Value (TTV) and Customer Lifetime Value (CLTV). Responsibilities include identifying and framing value through AI-augmented use cases, building business cases for SaaS ROI, driving engagement success with predictive AI models, orchestrating improvements using AI agents, and codifying best practices into scalable SaaS assets and reusable AI models. The role also involves providing feedback to product teams on machine learning and generative AI features for the CPG vertical.

What you'd actually do

  1. Translate Strategy to Operations: Analyze the client’s strategic priorities (e.g., Omnichannel Growth, Margin Protection, Supply Chain Agility, and Sustainability) and map them to high-impact, AI-augmented Celonis use cases.
  2. Target High-Impact Areas: Architect solutions that connect data across the value chain—moving beyond simple task automation to leveraging machine learning and process intelligence to optimize complex ecosystems like Demand Planning, Trade Promotions Management (TPM), Logistics, and Direct-to-Consumer (D2C) fulfillment.
  3. Build the Business Case: Construct robust, data-backed business cases that articulate the financial impact and SaaS ROI of Process Intelligence (e.g., reducing Cost of Goods Sold, optimizing working capital, or improving OTIF delivery) to senior management.
  4. Drive Engagement Success: Lead the value workstream for pilots and implementations, ensuring the "digital twin" strategy and predictive AI models effectively monitor process health in fast-paced, high-volume retail environments.
  5. Codify Best Practices: Turn our solutions into scalable SaaS assets, reusable AI models, and use cases for the broader Value Engineering team.

Skills

Required

  • Deep Industry Exp
  • Consulting
  • Analytical skills
  • AI-solution mindset

Nice to have

  • Process Mining
  • SaaS adoption
  • Value Engineering
  • Business Case Development
  • Customer Success
  • Sales Enablement

What the JD emphasized

  • AI-driven automation
  • Process Intelligence and AI capabilities
  • driving rapid Time-to-Value (TTV) and maximizing Customer Lifetime Value (CLTV)
  • enterprise-wide adoption of our platform
  • AI-augmented
  • leveraging machine learning and process intelligence
  • SaaS ROI
  • AI integration potential
  • predictive AI models
  • AI agents
  • intelligent process enhancement
  • scalable SaaS assets, reusable AI models
  • how to position AI capabilities
  • machine learning and generative AI features
  • scalable, cloud-native transformation engine
  • AI-driven use cases

Other signals

  • AI-driven automation
  • Process Intelligence and AI capabilities
  • leveraging machine learning and process intelligence
  • predictive AI models
  • AI agents
  • AI integration potential
  • scalable SaaS assets, reusable AI models
  • machine learning and generative AI features
  • AI-driven use cases