Senior Data & Applied Scientist Ontologies & Semantics

The context engine that makes AI enterprise ready.

Anyone can build an AI agent. What makes SAPs agents different is accuracy grounded in the richest enterprise data and process context in the world. As a SeniorData and Applied Scientist at SAPyoullbuild the context enginegrounded in SAPs Business ontology: the semantic infrastructure that transforms raw business data into the knowledge layer powering SAPs AI agents and assistants.

What youll build

The semantic and contextual foundation of SAPs 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. Youll 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 variousexternal providers (such asSalesforce Workday ServiceNow) andMES/IoT systems into unified semantic layers.

• Build AI capabilitiesincluding RAG pipelines embeddings vector databases and enterprise knowledgegrounding thatmake SAPs agentsaccurateand reliable in production.

• Develop AI capabilitiesincluding generative AI and LLM-based solutions using enterprise business data knowledge graphs business process intelligence and other structured and unstructured data assets.

• Leverage SAPs deep data and processcontextincludingSAP 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 platformsincluding Databricks SAP Datasphere SAP HANA Cloud AWS Azure and GCP to support reliable scalable AI workflows.

• Partner across product engineering business and customer-facing teamsto translate ambiguous business challenges into concrete AI solutions from concept through deployment and continuous improvement.

• Apply machine learning deep learning and statistical modelingto develop and evaluate AI solutions using real-world enterprise datasets.

What youll bring

Required Qualifications

• 5 years of experience in knowledge engineering semantic data systems applied AI or data science in industry research labs or advanced academic environments.

• Masters or PhDin Computer Science Applied Mathematics Statistics Engineering or a related quantitative field

• Hands-on experience designing enterprise ontologies and semantic models;proficiencyin 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 skillswith production-grade development practices; experience with ML libraries such asPyTorch TensorFlow or scikit-learn.

• Proventrack recorddeploying and operating AI/ML solutionsin production including handoff lifecycle support and continuous improvement.

• Experience with big data infrastructure and cloud environmentsDatabricks or equivalent plus at least one major cloud (AWS Azure or GCP).

• Excellent communication and stakeholder management skills with the ability to work cross-functionally in agile environments.

Preferred Qualifications

• Deep working knowledge of SAP data models metadata structures and core business processes end-to-end.(SAP knowledge is a strong accelerator)

• 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.

• Deepexpertiseacross the W3C stack (OWL RDF/RDFS SKOS SHACL) and/or property graph query languages (Cypher GQL).

• Deepexpertisein 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.

Where you belong

The Application AI team sits at the foundation layer - We build the LLM systems and intelligent infrastructure that run across SAPs global platforms which means the work you do here doesnt 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 robust data ontologies and semantic knowledge frameworks that give models real context about how SAPs 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 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 slowing you down.

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