Data Science Chief Expert, Finance

SAP
Palo Alto
Workplace: HybridFull timeUSD 274,300 - 609,200 annuallyFunction: Data Science & Machine LearningExperience: 10+ yearsSkills: ["Communication","Collaboration","Customer-facing skills","Analytical thinking","Cross-functional stakeholder management"]

Build and scale SAP’s semantic and contextual foundation for AI agents and assistants across enterprise business master data and end-to-end process semantics. Leverage SAP data models, ontologies, and semantic models to harmonize SAP and non-SAP sources into unified knowledge layers, then design, develop, deploy, and operationalize production ML/AI solutions using cloud and data platforms. Partner cross-functionally to translate business problems into measurable AI outcomes.

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FursaFursa
SAP
SAP
2 hours ago

Data Science Chief Expert, Finance

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Last checked: 2 hours agoStatus: Live

Job Summary

Build and scale SAP’s semantic and contextual foundation for AI agents and assistants across enterprise business master data and end-to-end process semantics. Leverage SAP data models, ontologies, and semantic models to harmonize SAP and non-SAP sources into unified knowledge layers, then design, develop, deploy, and operationalize production ML/AI solutions using cloud and data platforms. Partner cross-functionally to translate business problems into measurable AI outcomes.
Location: Palo Alto
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Leverage SAP data models, metadata structures, and business process semantics to build AI and semantic data solutions across enterprise domains.
  • •Design, develop, evaluate, and operationalize end-to-end ML/AI solutions from preprocessing and feature engineering through deployment, lifecycle support, and continuous improvement.
  • •Develop AI capabilities using enterprise business data, knowledge graphs, business process intelligence, and structured/unstructured data assets.
  • •Create and maintain enterprise ontologies and semantic models to improve interoperability, entity consistency, and business context across SAP and non-SAP data landscapes.
  • •Partner with product, engineering, business, and customer-facing teams to ensure AI solutions are scalable, practical, and production-ready.
Travel: Low travel

Pay and Benefits

Salary: USD 274,300 - 609,200 annually

Key Requirements

  • •Master’s degree or PhD in a quantitative field such as Computer Science, Applied Mathematics, Statistics, Engineering, or related areas.
  • •10+ years of experience with machine learning, deep learning, statistical modeling, generative AI, and LLMs, including hands-on model development, evaluation, and improvement using real-world datasets.
  • •Strong Python and SQL skills with production-grade development and ML libraries such as PyTorch, TensorFlow, and scikit-learn.
  • •Demonstrated experience deploying and operating AI/ML solutions in production, including production handoff and lifecycle support.
  • •Experience with enterprise data/AI stacks including SAP data models, master data domains, and ontology/knowledge graph technologies (e.g., SAP Datasphere/HANA Cloud/Graph API) and semantic standards (OWL/RDF/SPARQL).
Experience:10+ yearsEnterprise softwareAIMachine learningSemantic dataKnowledge graphs
Skills:CommunicationCollaborationCustomer-facing skillsAnalytical thinkingCross-functional stakeholder management
Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnDatabricksSAP DatasphereSAP HANA CloudSAP HANA Cloud Knowledge Graph EngineSAP Business Data CloudSAP One Domain ModelSAP Graph APISAP Business Accelerator HubAWSAzureGoogle Cloud PlatformOWLRDFRDFSSKOS

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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