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

In this role you will design and maintain enterprise ontologies and semantic models to ground SAP’s AI agents in real business data. You will build scalable AI capabilities (RAG, embeddings, knowledge grounding) and integrate data from SAP and partner systems to deliver accurate enterprise AI solutions. You’ll work across product, engineering, and customer-facing teams to translate ambiguous business challenges into production-ready AI capabilities, shaping AI at enterprise scale.

• great benefits
• learning and skill growth
• focus on health and well-being
• flexible working models
• trust and autonomy
• team that supports growth

• Design and maintain enterprise ontologies and semantic models for grounded AI understanding
• Build AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding
• Develop AI solutions using structured and unstructured enterprise data, including knowledge graphs and business process intelligence
• Leverage SAP data models, metadata structures, and process semantics across core processes to ground AI solutions
• Work with cloud and data platforms (Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, GCP) to support reliable, scalable AI workflows
• Collaborate with product, engineering, and business teams to translate challenges into AI solutions from concept to deployment and continuous improvement
• Apply machine learning, deep learning, and statistical modeling to real-world enterprise datasets
• Deploy and operate AI/ML solutions in production with lifecycle support and continuous improvement

• 8+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science
• Master's or PhD in a related quantitative field
• Hands-on experience designing enterprise ontologies and semantic models; proficiency in at least one graph query language (SPARQL, Cypher, or GQL)
• Experience with GenAI systems (RAG, embeddings, vector databases, semantic retrieval, knowledge grounding)
• Strong Python and SQL skills with production-grade development; experience with PyTorch, TensorFlow, or scikit-learn
• Proven track record deploying and operating AI/ML solutions in production
• Experience with big data infrastructure and cloud environments (Databricks, AWS/Azure/GCP)
• Excellent communication and cross-functional collaboration in agile environments

• Excellent communication
• Stakeholder management
• Cross-functional collaboration
• GenAI systems (RAG, embeddings, vector databases)
• Knowledge graphs and semantic models
• Graph query languages (SPARQL, Cypher, or GQL)