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Website: celonis.com
Celonis SE is an American-German data processing company that offers software as a service (SaaS) to improve business processes. It is headquartered in Munich, Germany with a secondary headquarters New York, United States.
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Celonis is hiring a Lead Value Engineer—Life Sciences for our New York office in a full-time role. You'll transform pharmaceutical and medical device supply chains by architecting AI solutions on our Process Intelligence Platform, partnering directly with Chief Supply Chain Officers to unlock operational value at scale. What This Role Involves You'll work with top-tier life sciences clients on their most complex supply chain challenges—from global logistics and raw material sourcing to quality control and regulated distribution. Using Celonis Process Intelligence combined with advanced AI technologies, you'll identify where autonomous decision-making and intelligent automation can deliver measurable business impact, then architect, prototype, and implement these solutions end-to-end. Your Responsibilities Conduct AI discovery and solutioning across customer supply chain operations—Sales & Operations Planning, Procure-to-Pay, Track & Trace, Cold Chain Management, Quality Control. Lead technical presales activities and remain embedded through implementation to drive adoption and value realization. Build AI prototypes in customer hackathons using generative AI, retrieval-augmented generation, multi-agent systems, and large language model technologies to solve critical operational pain points. Design and deliver proof-of-value projects integrating with enterprise ERPs like SAP and regulatory frameworks including FDA and GxP standards. Enable customers to deploy agentic process automation, shifting from rule-based systems to autonomous, AI-powered operations. Serve as the organization's technical expert on life sciences supply chain operations and pharmaceutical manufacturing. What We're Looking For 8+ years of technical pre-sales and implementation leadership in pharmaceutical or life sciences supply chain roles. Deep expertise in life sciences supply chain processes and the ability to translate business needs into AI use cases. Proficiency in generative AI—retrieval-augmented generation, prompt engineering, few-shot learning, multi-agent orchestration—applied to real business problems. Working knowledge of Python and ML libraries including LangChain, pandas, scikit-learn, and PyTorch; familiarity with data engineering tools for large datasets. Strong presentation and communication skills for technical and executive audiences. Bachelor's degree required; Master's degree in computer science, supply chain management, engineering, or related field preferred. Nice to Have: Hands-on agentic system development with safety evaluation for regulated environments; knowledge of supply chain data standards such as GS1 EPCIS; experience with LLM orchestration tools like LlamaIndex;…
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