Senior Data Scientist- Finance & Spend, Data Labs

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 patterns, 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 to ground AI solutions in real enterprise reality.
• Work with cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP to support reliable, scalable AI workflows.
• Partner across product, engineering, business, and customer-facing teams to translate ambiguous business challenges into concrete AI solutions from concept through deployment and continuous improvement.
• Apply machine learning, deep learning, and statistical modeling to develop and evaluate AI solutions using real-world enterprise datasets.

• 8+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science in industry, research labs, or advanced academic environments.
• Master's or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or 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); understanding of trade-offs between RDF triple stores and property graph databases.
• Hands-on experience with modern GenAI systems RAG, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding. Strong Python and SQL skills with production-grade development practices; experience with ML libraries such as PyTorch, TensorFlow, or scikit-learn.
• Proven track record deploying and operating AI/ML solutions in production including handoff, lifecycle support, and continuous improvement.
• Experience with big data infrastructure and cloud environments Databricks or equivalent, plus at least one major cloud (AWS, Azure, or GCP).
• Excellent communication and stakeholder management skills, with the ability to work cross-functional in agile environments.

• Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end.
• Hands-on experience with the SAP data and AI platform stack SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub.
• Deep expertise across the W3C stack (OWL, RDF/RDFS, SKOS, SHACL) and/or property graph query languages (Cypher, GQL).
• Experience on Financial (example - accounting, close, reporting) and Spend (procurement, s2p, contracts) domain knowledge.
• Deep expertise in machine learning and deep learning, with experience developing, evaluating, and improving models on real-world datasets.
• Experience with agentic AI, reasoning frameworks, planning, orchestration, tool use, or multi-agent architectures.
• Experience contributing to reusable AI platforms, foundation model initiatives, or shared AI services adopted across multiple product areas.
• Ability to design upper-level and mid-level ontologies aligned with industry standards and apply semantic interoperability frameworks across complex application landscapes.