Data Science Expert (Domain Models) - Data Labs (m/f/d)

At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.

The context engine that makes AI enterprise ready. Anyone can build an AI agent. What makes SAP's agents different is accuracy grounded in the richest enterprise data and process context in the world. As a Data and Applied Scientist at SAP, you'll build the context engine grounded in SAP's Business ontology: the semantic infrastructure that transforms raw business data into the knowledge layer powering SAP's AI agents and assistants.

The semantic and contextual foundation of SAP's AI. While generic AI agents operate on surface-level team, SAP agents are accurate because they understand the real semantics of enterprise business master data, process flows, and domain relationships. You'll build and scale the layer that makes that possible.

• Design and maintain enterprise ontologies and semantic models that give AI agents accurate, grounded understanding of SAP and connected business landscapes harmonizing data from SAP, Salesforce, Workday, ServiceNow, MES/IoT systems, and external providers into unified semantic layers.
• Build AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding that make SAP's agents accurate and reliable in production.
• Develop AI capabilities including generative AI and LLM-based solutions using enterprise business data, knowledge graphs, business process intelligence, and other structured and unstructured data assets.
• Leverage SAP's deep data and process context including SAP data models, metadata structures, and business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce functions.
• Work with cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP.
• Participate across product, engineering, business, and customer-facing teams.
• Apply machine learning, deep learning, and statistical modeling.

• 8+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science in industry.
• Master's or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or related field.
• Hands-on experience with ontologies.
• Hands-on experience with modern GenAI systems.
• Strong Python and SQL skills.
• Proven track record deploying AI/ML solutions.
• Experience with big data infrastructure.
• Excellent communication and stakeholder management skills.

• Deep working knowledge of SAP data models.
• Hands-on experience with SAP data and AI platform stack.
• Deep expertise across the W3C stack.
• Deep expertise in machine learning and deep learning.
• Experience with agentic AI.
• Experience with contributing to reusable AI platforms.
• Ability to design upper-level and mid-level ontologies.

The Application AI team sits at the foundation layer - We build the LLM systems and intelligent infrastructure that run across SAP's global platforms. This means the work you do here doesn't just influence one product, it sets the direction for how AI operates at enterprise scale. A core part of that challenge is making AI genuinely understand the business - not just process text, but reason over richly structured enterprise data through solid data ontologies and semantic knowledge frameworks because that gives models real context about how SAP's world is organized.

This is a team that values engineers who think like owners: people who want to define the architecture, not just implement a spec. You'll work in an environment designed around trust and autonomy, where the expectation is that you move fast, make calls, and drive outcomes without layers of approval grounding you down.

AI skills used in this role: Agentic AI Day-to-Day Practice, AI Adoption Capability, AI Knowledge Management, Context Engineering, AI-Assisted Automation and Prototyping, Learning Agility, Creative Thinking, Complex Problem Solving, Effective Communication, Collaboration, Agentic Orchestration, Data Engineering, Deep Learning, Model Training, Semantic Retrieval