Data Science Expert (Supply Chain Management) - Data Labs (m/f/d)

SAP
Munich
Workplace: HybridFull timeFunction: Data Science & Machine LearningEducation: phdSkills: ["Communication","Stakeholder management","Cross-functional collaboration","Agile collaboration","Complex problem solving"]

Build the semantic context engine that powers SAP’s enterprise AI agents for autonomous supply-chain capabilities. Design and maintain ontologies and semantic models, then develop and productionize GenAI/LLM solutions using RAG, embeddings, vector databases, and knowledge grounding. Apply ML, deep learning, and statistical modeling to real enterprise datasets, working with cloud data platforms and cross-functional teams to deploy scalable, accurate AI workflows.

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SAP
SAP
2 hours ago

Data Science Expert (Supply Chain Management) - Data Labs (m/f/d)

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Last checked: 2 hours agoStatus: Live

Job Summary

Build the semantic context engine that powers SAP’s enterprise AI agents for autonomous supply-chain capabilities. Design and maintain ontologies and semantic models, then develop and productionize GenAI/LLM solutions using RAG, embeddings, vector databases, and knowledge grounding. Apply ML, deep learning, and statistical modeling to real enterprise datasets, working with cloud data platforms and cross-functional teams to deploy scalable, accurate AI workflows.
Location: Munich
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Design and maintain enterprise ontologies and semantic models that provide AI agents accurate, grounded understanding of business landscapes.
  • •Build AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding for production AI agents.
  • •Develop GenAI and LLM-based solutions using enterprise business data, knowledge graphs, business process intelligence, and structured/unstructured assets.
  • •Leverage SAP data models, metadata structures, and business process semantics across order-to-cash, procure-to-pay, record-to-report, and plan-to-produce.
  • •Apply machine learning, deep learning, and statistical modeling to develop, evaluate, and continuously improve AI solutions on real-world enterprise datasets.
Travel: Low travel

Key Requirements

  • •8+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science.
  • •Master’s or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or related quantitative field.
  • •Hands-on experience designing enterprise ontologies and semantic models, including proficiency in at least one graph query language (SPARQL, Cypher, or GQL).
  • •Hands-on experience with modern GenAI systems such as RAG, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding.
  • •Strong Python and SQL skills, plus experience deploying and operating AI/ML solutions in production.
Experience:Supply chainKnowledge engineeringSemantic data systemsGenAIEnterprise AI
Education:PhD / Doctorate in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field
Skills:CommunicationStakeholder managementCross-functional collaborationAgile collaborationComplex problem solving
Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnRAGEmbeddingsVector databasesGraph query languageSPARQLCypherGQLRDFRDFSOWLSKOSSHACLDatabricksSAP DatasphereSAP HANA Cloud

Company Brief

SAP
Global enterprise software company best known for ERP systems and business applications covering finance, supply chain, procurement, HR, analytics, and customer management for large organizations.
Industry: Enterprise Software
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Walldorf, Germany
Founded: 1972
Glassdoor
Glassdoor: 4.1
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