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 Data and Applied Scientist at SAPyoullhelp build the context engine grounded in SAPs Business Ontology: the semantic infrastructure that transforms raw business data into the knowledge layer powering SAPs AI agents and assistants.

This is an early-career role for engineers and scientists who are sharp curious and ready to do real work on hard problems from day one.

Whatyoullbuild

Youllcontribute to the semantic and contextual foundation of SAPs AI. While generic AI agentsoperateon surface-level patterns SAP agents areaccuratebecause they understand the real semantics of enterprise business master data process flows and domain alongside senior scientists and engineers to build and scale the layer that makes that possible.

• Support the design and maintenance of enterprise ontologies and semantic modelsthat give AI agents accurate grounded understanding of SAP and connected business landscapes learning how data from SAP Salesforce Workday ServiceNow MES/IoT systems and external providers gets harmonized into unified semantic layers.

• Contribute to AI capabilitiesincluding RAG pipelines embeddings vector databases and enterprise knowledge grounding that make SAPs agentsaccurateand reliable in production.

• Develop and iterate on AI solutions including generative AI and LLM-based approaches using enterprise business data knowledge graphs business process intelligence and structured and unstructured data assets.

• Learn SAPs deep data and process context data models metadata structures and business process semantics across Order-to-Cash Procure-to-Pay Record-to-Report and Plan-to-Produce and apply that context to ground AI solutions in real enterprise reality.

• Work with modern cloud and data platformsincluding Databricks SAP Datasphere SAP HANA Cloud AWS Azure and GCP gaining hands-on experience with scalable AI workflows.

• Collaborate across product engineering and business teamsto understand how ambiguous business challenges get translated into concrete AIsolutions andcontribute meaningfully to that process fromearly stagesthrough deployment.

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

Whatyoullbring

Required Qualifications

• Bachelors orMasters in Computer Science Applied Mathematics Statistics Engineering or a related quantitative field.

• 2 years of Machine Learning Computer Science Computer Engineering or related field experience work.

• Foundational understanding of knowledge representation semantic data systems or graph databases (through coursework research or personal projects).

• Familiarity with at least one graph query language (SPARQL Cypher or GQL) or a willingness to learn quickly; some exposure to the trade-offs between RDF triple stores and property graph databases is a plus.

• Exposure tomodern GenAI concepts RAG embeddings vector databases semantic retrieval through coursework research or hands-on experimentation.

• Solid Python and SQL skills; some experience with ML libraries such asPyTorch TensorFlow or scikit-learn (academic projects research work and personal projects all count).

• Eagerness to learn production-grade development practices and grow into operating AI/ML solutions end-to-end.

• Clear collaborative communication style you ask good questions explain your thinking and work well with others.

Preferred Qualifications

• Hands-on experience through internships research or projects with ontology design semantic modeling or knowledge graphs.

• Any exposure to enterprise software ecosystems (SAP Salesforce Workday ServiceNow or similar) is a real accelerator here.

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

• Academic or project experience in machine learning and deep learning including training evaluating and improving models on real datasets.

• Curiosity about agentic AI reasoning frameworks multi-agent architectures or planning and orchestration.

• Experience contributing to shared or reusable codebases open-source projects research codebases or team projects.

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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