Senior Data Scientist- Finance & Spend, Data Labs

SAP
Bengaluru
Workplace: HybridFull timeFunction: Data Science & Machine LearningExperience: 5+ yearsSkills: ["Communication","Stakeholder management","Cross-functional collaboration","Agile collaboration"]

Build and scale SAP’s semantic and contextual foundation for AI agents used in Finance and Spend. Design enterprise ontologies and semantic models, harmonize data into unified semantic layers, and develop production-grade GenAI capabilities using RAG, embeddings, and vector databases. Apply ML and deep learning on real enterprise datasets and collaborate across product, engineering, and business teams to deploy and continuously improve AI workflows on modern cloud and data platforms.

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FursaFursa
SAP
SAP
2 hours ago

Senior Data Scientist- Finance & Spend, Data Labs

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Last checked: 2 hours agoStatus: Live

Job Summary

Build and scale SAP’s semantic and contextual foundation for AI agents used in Finance and Spend. Design enterprise ontologies and semantic models, harmonize data into unified semantic layers, and develop production-grade GenAI capabilities using RAG, embeddings, and vector databases. Apply ML and deep learning on real enterprise datasets and collaborate across product, engineering, and business teams to deploy and continuously improve AI workflows on modern cloud and data platforms.
Location: Bengaluru
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 SAP AI agents using SAP and connected enterprise business context.
  • •Build and scale AI capabilities including RAG pipelines, embeddings, vector databases, semantic retrieval, 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 data 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 to ground AI solutions.
  • •Apply machine learning, deep learning, and statistical modeling to develop, evaluate, and continuously improve AI solutions using real-world enterprise datasets.
Travel: Low travel

Key Requirements

  • •5+ 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, including proficiency in at least one graph query language (SPARQL, Cypher, or GQL) and understanding RDF vs property graph trade-offs.
  • •Hands-on GenAI systems experience with RAG, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding; strong Python and SQL with production-grade practices and ML libraries such as PyTorch, TensorFlow, or scikit-learn.
  • •Proven track record deploying and operating AI/ML solutions in production, including lifecycle support and continuous improvement.
Experience:5+ yearsApplied AIKnowledge engineeringData scienceGenAIEnterprise data
Education:
Skills:CommunicationStakeholder managementCross-functional collaborationAgile collaboration
Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnRAGEmbeddingsVector databasesSPARQLCypherGQLRDFRDFSOWLSKOSSHACLDatabricksSAP DatasphereSAP HANA CloudAWS

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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