Data Science Expert (Supply Chain Management) - Data Labs (m/f/d)

As a Data and Applied Scientist at SAP, you will shape the semantic foundation powering SAP's AI agents by building enterprise ontologies and semantic models. You’ll ground AI in real business data and processes across global landscapes, enabling accurate, scalable AI capabilities. You’ll collaborate with cross-functional teams to translate ambiguous challenges into concrete ML-driven solutions. This role offers the chance to impact supply-chain AI and enterprise decisioning at scale with cutting-edge knowledge graphs and RAG-driven pipelines.

• great benefits
• learning and skill growth
• flexible working models
• well-being focus
• inclusion and belonging
• career development

• Design and maintain enterprise ontologies and semantic models
• Build AI capabilities including RAG pipelines, embeddings, and vector databases
• Develop AI solutions using enterprise data, knowledge graphs, and business process semantics
• Ground AI in SAP data models, metadata structures, and process semantics across key end-to-end processes
• Work with cloud and data platforms (Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, GCP) to enable scalable AI workflows
• Collaborate with product, engineering, and customer-facing teams from concept to deployment and continuous improvement
• Apply ML, deep learning, and statistical modeling to real-world enterprise datasets

• 8+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science
• Master's or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or related quantitative field
• Hands-on experience designing enterprise ontologies and semantic models; proficiency in SPARQL, Cypher, or GQL; understanding RDF triple stores vs property graphs
• Experience with GenAI systems, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding
• Strong Python and SQL with production-grade practices; ML libraries such as PyTorch, TensorFlow, or scikit-learn
• Proven track record deploying and operating AI/ML solutions in production with lifecycle support
• Experience with big data infrastructure and cloud environments (Databricks, AWS/Azure/GCP)
• Excellent communication and stakeholder management, agile cross-functional collaboration

• clear communication
• stakeholder management
• collaboration across teams
• SPARQL
• Cypher
• GQL