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Celonis is seeking an Applied Value Engineer to drive customer success and value realization for enterprise clients using its Process Intelligence platform. In this role, you will serve as a trusted advisor to strategic customers, guiding them through their entire value journey—from pre-sales discovery and technical demonstrations through post-sale implementation and adoption.
You will own the end-to-end customer lifecycle, managing weekly cadences, quarterly business reviews (QBRs), and value plans with VP+ level executives. Your responsibilities include leading technical discovery sessions, analyzing customer data to identify optimization opportunities, and deploying solutions that address critical operational challenges such as legacy system modernization, finance process optimization, and intelligent autonomous agent deployment.
A key aspect of this role is translating customer pain points into innovative solutions using Celonis's Process Intelligence Graph and AI/LLM capabilities. You will establish repeatable frameworks for customers to independently track and expand business value, while serving as an internal and external subject matter expert for your assigned customer industry domain.
You will collaborate closely with Sales Teams and leverage the Celonis Ecosystem partner network to manage multiple concurrent projects at different stages of the value journey. Strong project management skills are essential, as you'll need to balance scope, expectations, and timelines across a portfolio of accounts.
Required qualifications include 7+ years of experience in pre-sales, customer success, or consulting roles, with a background in business process improvement, digital transformation, or customer value realization. You must have strong technical skills across Microsoft Azure or equivalent platforms (AWS), understanding of data engineering concepts, and hands-on knowledge of LLMs and prompt engineering. A Bachelor's degree in computer science, engineering, mathematics, or related field is required.
Nice-to-have skills include understanding of business processes in Supply Chain or Finance, proficiency in Python and ML libraries (LangChain, pandas, sklearn, PyTorch), and hands-on experience building agentic systems using LLM orchestration, RAG, and function calling.
