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# Data Science Expert (Domain Models) - Data Labs

- **Company:** Sap's Ai.
- **Location:** München, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Google App Engines, Microsoft Azure, Big Data, Cloud Computing, Information Engineering, Data Systems, Query Languages, Graph Database, Python (Programming Language), Machine Learning, Resource Description Framework (RDF), Tensorflow, Standard Sql, SAP (Applications), SAP Business Suiteing, SAP HANA, SAP Knowledge Warehouse, SAP NetWeaver Data Management, SPARQL, Unstructured Data, Google Cloud, Cloud Platform System, Pytorch, Large Language Models, Multi-Agent Systems, Deep Learning, AI Platforms, Scikit Learn, Information Technology, Graphql, Data Management, Virtual Agents, Domain Model, Databricks
- **Published:** August 30, 2026
- **Apply:** https://www.careerjet.de/jobad/def87821cf9baa20ada0184f37e2d8b590

## About the Role

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-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.
Deep expertise across the W3C stack (OWL, RDF/RDFS, SKOS, SHACL) and/or property graph query languages (Cypher, GQL).
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.
Where you belong
The Application AI team sits at the foundation layer - We build the LLM systems and intelligent infrastructure that run across SAP's global platforms, which means the work you do here doesn't 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 SAP's 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 spec. You'll work 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.
AI skills used in this role: Agentic AI Day-to-Day Practice, AI Adoption Capability, AI Output Quality Assurance, Context Engineering, AI-Assisted Automation and Prototyping, Learning Agility, Creative Thinking, Complex Problem Solving, Effective Communication, Collaboration, Agentic Orchestration, Data Engineering, Deep Learning, Model Training, Semantic Retrieval
#DLhiring

## Description

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.


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