Data & Applied Scientist - Ontologies & Semantics

Data & Applied Scientist - Ontologies & Semantics

We help the world run better
At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging - but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong.

What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.

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'llhelp 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.

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.

You'll contribute to the semantic and contextual foundation of SAP's 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 relationships.

You'llwork 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 models that give AI agents accurate, grounded understanding of SAP and connected business landscapes - learning how data from SAP, Salesforce, Workday, Service Now, MES/IoT systems, and external providers gets harmonized into unified semantic layers.

Contribute to AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding that make SAP's 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 SAP's 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 platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP, gaining hands‑on experience with scalable AI workflows.

Collaborate across product, engineering, and business teams to understand how ambiguous business challenges get translated into concrete AI solutions, and contribute meaningfully to that process fromearly stages through deployment.

Apply machine learning, deep learning, and statistical modeling to build and evaluate AI solutions using real‑world enterprise datasets.

Bachelor's or Master's 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, Tensor Flow, 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…