Lead Value Engineer - Life Sciences

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

Lead Value Engineer for Life Sciences at Celonis, focusing on leveraging Process Intelligence and AI/ML to solve business-critical supply chain problems. The role involves understanding client needs, prototyping AI solutions (including agentic systems with RAG and LLMs), demonstrating value to executives, and ensuring successful implementation. It requires expertise in generative AI techniques, Python, ML libraries, and supply chain processes within the life sciences sector, with a strong emphasis on delivering measurable impact and ROI through AI deployments.

What you'd actually do

  1. Understand the client's overarching AI strategy and the distinct supply chain challenges across both their MedTech portfolios (e.g., mitigating global raw material shortages, optimizing supply chains, managing inventories, or accelerating quality batch releases). As a Celonis product and life sciences domain expert, translate these complex, multi-tiered logistics requirements into innovative AI solutions that drive measurable impact.
  2. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds in the supply chain are successfully reached.
  3. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points in planning, sourcing, manufacturing, and distribution.
  4. Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., autonomous inventory rebalancing or intelligent shipment exception handling).
  5. End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise ERPs (e.g., SAP), Quality Management Systems (QMS), and strict regulatory frameworks (FDA, EMA, GxP).

Skills

Required

  • 8+ years of experience leading technical pre-sales and post-sales engagements specifically within Life Sciences, Pharmaceutical, or MedTech supply chains.
  • Defining AI roadmaps
  • building compelling ROI/TCO business cases
  • guiding technical implementations through to value realization.
  • Deep understanding of supply chain business processes native to Life Sciences (such as Sales & Operations Planning (S&OP), Procure-to-Pay, Track & Trace, Cold Chain Management, or Quality Control/Batch Release)
  • translate high-level business needs into specific AI use cases.
  • Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent chatbots for supplier collaboration, automated extraction of data from complex customs or quality documents).
  • Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch)
  • data engineering tools and technologies for handling massive, siloed supply chain datasets.
  • Strong presentation skills to both internal and external stakeholders (including supply chain executives and IT leaders), whether leading technical whiteboarding sessions or formal readouts and demos.
  • Bachelor’s Degree required

Nice to have

  • Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations suited for highly regulated (GxP) life sciences environments.
  • Familiarity with life sciences supply chain data standards and systems (e.g., GS1 EPCIS for traceability, SAP APO/IBP, Kinaxis).
  • Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
  • Experience in deploying an
  • Master's Degree in computer science, supply chain management, engineering, mathematics, or related fields, or equivalent work experience preferred.

What the JD emphasized

  • 8+ years of experience leading technical pre-sales and post-sales engagements specifically within Life Sciences, Pharmaceutical, or MedTech supply chains.
  • Deep understanding of supply chain business processes native to Life Sciences
  • Expertise in generative AI techniques
  • Solid knowledge of Python and common ML libraries
  • strict regulatory frameworks (FDA, EMA, GxP)

Other signals

  • industrialize AI
  • unlock ROI on AI deployments at scale
  • autonomous AI agents
  • secure, scalable LLM/agent systems