Lead Value Engineer - Life Sciences

Celonis is the trusted platform to industrialize Enterprise AI. At our core is the Celonis Context Model — which combines process data, business knowledge, and intelligence into a living digital twin of the enterprise that AI can actually understand, turning AI's operational blind spots into operational clarity. World's leading companies trust Celonis and its global ecosystem of partners to make their AI agents, people, systems, and processes work together, achieving transformational outcomes. We believe there's a massive opportunity to unlock global productivity and sustainability by giving AI the context to understand how every business process really works. Join our mission to make processes work for people, companies, and the planet. Role Description As a Lead Value Engineer specializing in the Life Sciences, you are pushing the envelope in solving business-critical problems for the world's largest, most diversified life science organizations. You will be working intimately with this strategic client, understanding their uniquely complex objectives—spanning from logistics to the precision distribution of advanced products—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners (e.g., Microsoft, OpenAI, Databricks).. With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the intricate realities of our customers’ business operations and enables them to industrialize AI. This unlocks real ROI on AI deployments at scale, ensuring life-saving products reach patients faster and safer. There is no AI without PI. You will prototype these solutions, demonstrate their value to customer business and IT leadership, and drive value realization to increase the footprint of Celonis across the healthcare and life sciences sector. Key Responsibilities AI Discovery & Solutioning: Understand the client's overarching AI strategy and the distinct challenges that are distinct to their business. As a Celonis product expert, translate these complex, multi-tiered requirements into innovative AI solutions that drive measurable impact.. Predictive Modeling and Optimization: Leverage data stored within the Celonis context model to design and deploy advanced time-series forecasting, optimization, and predictive algorithms to solve complex logistics constraints (e.g., dynamic inventory routing, schedule optimization). Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. 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. Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex, fragmented supply chain networks. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points in planning, sourcing, manufacturing, and distribution. Agentic Process Transformation: 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). Proof Projects: 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 systems of record. Requirements 8+ years of experience leading technical pre-sales and post-sales engagements preferred within Healthcare, Life Sciences, Pharmaceutical, or MedTech. This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization. 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 Advanced Predictive Analytic techniques (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies for leveraging massive, siloed datasets to build algorithms and tools aimed at predicting future business outcomes. 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; Master's Degree in computer science, supply chain management, enginee