Data Science Expert- Finance & Spend, Data Labs

SAP
Bengaluru
Workplace: HybridFull timeFunction: Data Science & Machine LearningExperience: 8+ yearsEducation: mastersSkills: ["Communication","Stakeholder management","Cross-functional collaboration","Agile collaboration"]

Build and scale the semantic and contextual foundation powering SAP’s AI agents for Finance and Spend. Design enterprise ontologies and semantic models that unify data from SAP and connected systems, and develop GenAI/LLM capabilities including RAG pipelines, embeddings, vector databases, and knowledge-grounded retrieval. Apply ML/deep learning and statistical modeling to build, deploy, and continuously improve production AI workflows across SAP cloud and major hyperscalers.

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SAP
SAP
2 hours ago

Data Science Expert- Finance & Spend, Data Labs

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Job Summary

Build and scale the semantic and contextual foundation powering SAP’s AI agents for Finance and Spend. Design enterprise ontologies and semantic models that unify data from SAP and connected systems, and develop GenAI/LLM capabilities including RAG pipelines, embeddings, vector databases, and knowledge-grounded retrieval. Apply ML/deep learning and statistical modeling to build, deploy, and continuously improve production AI workflows across SAP cloud and major hyperscalers.
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 enable accurate, grounded understanding for SAP AI agents across SAP and connected business landscapes.
  • •Build and scale AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge-grounding for production-ready agent accuracy.
  • •Develop generative AI 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.
  • •Work with cloud and data platforms (including Databricks and SAP cloud services) and partner across teams to translate business challenges into deployed AI solutions with continuous improvement.
Travel: Low travel

Key Requirements

  • •8+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science in industry or advanced academic/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; proficiency in at least one graph query language (SPARQL, Cypher, or GQL) and understanding of RDF triple stores vs property graphs.
  • •Hands-on experience with GenAI systems such as RAG, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding.
  • •Strong Python and SQL skills with production-grade practices, including experience with ML libraries such as PyTorch, TensorFlow, or scikit-learn; experience deploying AI/ML in production.
Experience:8+ yearsKnowledge engineeringSemantic dataApplied AIData scienceEnterprise AIGenAIMLDeep learning
Education:Master's in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field
Skills:CommunicationStakeholder managementCross-functional collaborationAgile collaboration
Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnRAGEmbeddingsVector databasesSemantic retrievalKnowledge groundingGraph query language (SPARQL)Graph query language (Cypher)Graph query language (GQL)RDFRDFSOWLSKOSSHACLProperty graphDatabricks

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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