Senior Data Scientist (Supply Chain Management) - Data Labs (m/f/d)

SAP
Germany
Workplace: HybridFull timeFunction: Data Science & Machine LearningExperience: 5+ yearsEducation: mastersSkills: ["Communication","Stakeholder management","Cross-functional collaboration","Creative thinking","Complex problem solving"]

Build and scale SAP’s semantic “context engine” that grounds AI agents in enterprise business ontology and process context for autonomous supply-chain decisions. Design and maintain enterprise ontologies and semantic models, and develop GenAI/LLM solutions using RAG pipelines, embeddings, vector databases, and knowledge graphs. Work with SAP data models across key process areas and deploy production-grade AI/ML on cloud data platforms, partnering across engineering and product to deliver continuous improvements.

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SAP
SAP
2 hours ago

Senior Data Scientist (Supply Chain Management) - Data Labs (m/f/d)

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Last checked: 2 hours agoStatus: Live

Job Summary

Build and scale SAP’s semantic “context engine” that grounds AI agents in enterprise business ontology and process context for autonomous supply-chain decisions. Design and maintain enterprise ontologies and semantic models, and develop GenAI/LLM solutions using RAG pipelines, embeddings, vector databases, and knowledge graphs. Work with SAP data models across key process areas and deploy production-grade AI/ML on cloud data platforms, partnering across engineering and product to deliver continuous improvements.
Location: Germany
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 ground AI agents in SAP and connected business landscapes.
  • •Build and scale RAG pipelines and enterprise knowledge grounding using embeddings and vector databases for accurate, reliable AI in production.
  • •Develop GenAI and LLM-based solutions using enterprise business data, knowledge graphs, business process intelligence, and structured/unstructured data.
  • •Leverage 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.
  • •Apply ML/deep learning/statistical modeling on real enterprise datasets and deploy AI/ML solutions with handoff, lifecycle support, and continuous improvement.
Travel: Low travel

Key Requirements

  • •5+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science in industry or research 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, 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 with production-grade development practices, plus experience deploying AI/ML solutions in production.
Experience:5+ years
Education:Master's
Skills:CommunicationStakeholder managementCross-functional collaborationCreative thinkingComplex problem solving
Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnSPARQLCypherGQLRAGEmbeddingsVector databasesKnowledge graphsDatabricksSAP DatasphereSAP HANA CloudAWSAzureGCPOWLRDF

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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