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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Associate Value Engineer (AI-Driven Data Science & Analytics) - Orbit Program - **Company:** Celonics Inc - **Location:** New York, NY, United States - **Experience:** Starter - **Salary:** $90,000.0 - $95,000.0 - **Contract:** Internship / Graduate position - **Skills:** Artificial Intelligence, Big Data, Systems Analysis, Python (Programming Language), Machine Learning, Oracle (Applications), Power BI, SAP (Applications), SQL Databases, Tableau (Software), Value Engineering, Large Language Models, Information Technology, Data Analytics - **Published:** July 7, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=5045ba2c08f35e5e ## About the Role * Bachelor's degree in Business, Engineering, Data Analytics, Economics, Computer Science, Mathematics, or a related STEM field. * Extensive internship experience or 1-3 years of full-time work experience. * Hands-on experience with at least one of the following: SQL, Microsoft Power BI, Tableau, or another data analytics solution. * A strong passion for technology, big data, and its transformative potential. * Excellent analytical and creative problem-solving skills, with the ability to apply technology to business challenges. * A customer-centric mindset with a focus on delivering value. * The ability to build relationships with senior management and influence decision-making. * Confidence in presenting to diverse audiences. * A proactive approach to problem-solving, with a willingness to learn from mistakes. * Curiosity, self-motivation, a commitment to continuous learning, and the desire to thrive in a fast-paced, high-growth environment. * Strong organizational skills with the ability to prioritize and execute on deadlines. * Excellent communication skills and fluency in English. Bonus Points: * GPA of 3.6 or higher. * Degree in Industrial Engineering or a similar field. * Degrees combining technical (e.g., Computer Science, Data Science) and business (e.g., Economics, Marketing) skills. * Experience with Python and Machine Learning. * Customer-facing, customer success/service, or sales experience. * Experience with Proof of Concept projects. * Supply Chain process experience (Procurement, Order Management, Inventory Management, Production). * Finance process experience (Accounts Payable, Accounts Receivable). * Experience modeling ROI and TCO for business case justification. * Experience with SAP and/or Oracle. * Fluency in French and/or Spanish. ## Description * Consult with customers using systems analysis to identify core business challenges and determine how Celonis can drive impactful solutions. * Present the Celonis AI value proposition and vision to customers and align our solutions with their AI strategy. * Prototype and develop AI solutions tailored to customer needs, including both traditional machine learning and LLM-based solutions. * Execute proof projects showcasing the value of Celonis Process Intelligence in the context of our customers' strategy, business initiatives, and challenges * Develop and modify Celonis data models using SQL and analyses using PQL (Celonis proprietary language) to create compelling prototypes that address customer pain points. * Lead proof-of-concept projects within the Celonis platform to demonstrate tangible value aligned with customer strategies and needs. * Collaborate with customers and partners to design and build Celonis solutions that ensure a positive return on investment. * Establish strong, trusted advisor relationships with customers through your technical expertise, product knowledge, and business acumen. Articulate and quantify strategic business value for customers, delivering impactful presentations to senior executives. * Partner with the implementation services team to ensure successful value realization for customers. * Identify opportunities for innovation and collaborate with Product & Engineering teams to influence future product development. * Contribute to the Global Value Engineering Center of Excellence by creating reusable collateral, best practices, and tools. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents](https://www.wearedevelopers.com/videos/1547-data-analytics-with-microsoft-fabric-end-to-end-use-case-with-data-agents) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)