Overview As a Data and Applied Scientist at SAP, you will help build the semantic and contextual backbone for SAP's AI-grounding agents in real enterprise data and processes. You will design ontologies, fuse data from SAP and external systems, and scale AI capabilities like RAG, embeddings, and knowledge grounding. You'll work with cloud and data platforms to deliver reliable AI workflows and collaborate across product, engineering, and business teams. This role offers the chance to shape enterprise AI that directly supports SAP's supply-chain and business processes.
Compensation / Benefits- great benefits
- flexible working models
- focus on health and well-being
- opportunity for learning and growth
- collaboration across diverse teams
- inclusion-focused culture
Responsibilities- Design and maintain enterprise ontologies and semantic models to harmonize data from SAP, Salesforce, Workday, ServiceNow, MES/IoT, and more
- Develop AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding for production use
- Build generative AI and LLM-based solutions using enterprise data, knowledge graphs, and process intelligence
- Leverage SAP data models, metadata structures, and business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, Plan-to-Produce
- Work with cloud and data platforms (Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, GCP) to enable scalable AI workflows
- Collaborate with cross-functional teams to translate business challenges into AI solutions from concept to deployment and improvement
- Apply ML, deep learning, and statistical modeling to real-world enterprise datasets to evaluate AI solutions
- Design and deploy AI solutions with sound production lifecycle management and stakeholder communication
Key requirements- 5+ years in knowledge engineering, semantic data systems, applied AI, or data science
- Master's or PhD in a quantitative field
- Hands-on ontology and semantic model design; proficiency in SPARQL, Cypher, or GQL; understanding RDF vs property graph databases
- Experience with GenAI systems, embeddings, vector databases, and semantic grounding
- Strong Python and SQL; ML libraries such as PyTorch, TensorFlow, or scikit-learn
- Proven production deployment of AI/ML solutions with lifecycle support
- Experience with big data infrastructure and cloud environments (Databricks and at least one cloud: AWS/Azure/GCP)
- Excellent communication and cross-functional collaboration in agile settings
- excellent communication
- stakeholder management
- cross-functional collaboration
- SPARQL
- Cypher
- GQL
