Principal Data & Applied Scientist - Ontologies & Semantics (Palo Alto, CA, US, 94304)

SAP
Palo Alto
Workplace: HybridFull timeUSD 198,200 - 420,000 annuallyFunction: Research & Scientific (R&D)Experience: 8+ yearsEducation: phdSkills: ["Communication","Stakeholder management","Cross-functional collaboration","Agile teamwork"]

Build and scale SAP’s enterprise “context engine” that grounds AI agents in semantic business data and process meaning. Design and maintain enterprise ontologies and semantic models, integrate data across SAP and external providers into unified layers, and develop production AI capabilities such as RAG pipelines, embeddings, vector databases, and LLM/knowledge-graph solutions. Partner cross-functionally to turn business challenges into deployable, continuously improved AI systems using cloud data platforms.

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SAP
SAP
2 hours ago

Principal Data & Applied Scientist - Ontologies & Semantics (Palo Alto, CA, US, 94304)

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Job Summary

Build and scale SAP’s enterprise “context engine” that grounds AI agents in semantic business data and process meaning. Design and maintain enterprise ontologies and semantic models, integrate data across SAP and external providers into unified layers, and develop production AI capabilities such as RAG pipelines, embeddings, vector databases, and LLM/knowledge-graph solutions. Partner cross-functionally to turn business challenges into deployable, continuously improved AI systems using cloud data platforms.
Location: Palo Alto
Workplace: Hybrid
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Design and maintain enterprise ontologies and semantic models that enable accurate, grounded understanding for SAP’s AI agents and assistants.
  • •Build AI capabilities such as RAG pipelines, embeddings, vector databases, and semantic retrieval for reliable production knowledge grounding.
  • •Develop generative AI 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 key business areas to ground AI solutions in real enterprise context.
  • •Work with cloud and data platforms and partner across product, engineering, business, and customer teams to translate challenges into deployed AI solutions with continuous improvement.
Travel: Low travel

Pay and Benefits

Salary: USD 198,200 - 420,000 annually

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 a related quantitative field.
  • •Design enterprise ontologies and semantic models, with proficiency in at least one graph query language (SPARQL, Cypher, or GQL) and understanding RDF vs property graph trade-offs.
  • •Hands-on GenAI RAG experience, including embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding.
  • •Strong Python and SQL skills with production-grade development, plus experience deploying and operating AI/ML in production (handoff, lifecycle support, continuous improvement).
Experience:8+ years
Education:PhD / Doctorate in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field
Skills:CommunicationStakeholder managementCross-functional collaborationAgile teamwork
Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnSPARQLCypherGQLRDFRDFSOWLSKOSSHACLRAGEmbeddingsVector databasesLLMKnowledge graphsDatabricksSAP Datasphere

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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