Data Science Chief Expert, Supply Chain Management (Palo Alto, CA, US, 94304)

SAP
Palo Alto
Workplace: HybridFull timeUSD 274,300 - 609,200 annuallyFunction: Data Science & Machine LearningExperience: 10+ yearsEducation: mastersSkills: ["Communication","Collaboration","Agile development","Customer-facing skills"]

Build and scale an AI context engine grounded in SAP’s business ontology, turning enterprise master data and process semantics into knowledge that powers SAP AI agents and assistants. Work across ontology/semantic model design, entity resolution, and integration of SAP and non-SAP data. Develop, deploy, and operationalize end-to-end machine learning and generative AI/LLM solutions using cloud and data platforms, partnering with product, engineering, and business teams.

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SAP
SAP
2 hours ago

Data Science Chief Expert, Supply Chain Management (Palo Alto, CA, US, 94304)

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Last checked: 2 hours agoStatus: Live

Job Summary

Build and scale an AI context engine grounded in SAP’s business ontology, turning enterprise master data and process semantics into knowledge that powers SAP AI agents and assistants. Work across ontology/semantic model design, entity resolution, and integration of SAP and non-SAP data. Develop, deploy, and operationalize end-to-end machine learning and generative AI/LLM solutions using cloud and data platforms, partnering with product, engineering, and business teams.
Location: Palo Alto
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Leverage SAP data and process understanding to build AI and semantic data solutions across enterprise process domains such as Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce.
  • •Design and maintain enterprise ontologies and semantic models to improve interoperability, entity consistency, and business context across SAP and non-SAP data landscapes.
  • •Work with cloud and data platforms (e.g., Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, Google Cloud Platform) to support reliable AI workflows.
  • •Translate business challenges into AI use cases, technical designs, and measurable business outcomes.
  • •Design, develop, evaluate, and operationalize end-to-end machine learning and AI solutions from data preprocessing through deployment, production handoff, and continuous improvement.
Travel: Low travel

Pay and Benefits

Salary: USD 274,300 - 609,200 annually

Key Requirements

  • •Master’s degree or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field.
  • •10+ years of experience with machine learning, deep learning, statistical modeling, generative AI, and LLMs, including hands-on model development, evaluation, and improvement with real-world datasets.
  • •Strong Python and SQL skills, including production-grade Python development and experience with ML libraries such as PyTorch, TensorFlow, and scikit-learn.
  • •Experience deploying and operating AI or machine learning solutions in production environments, including production handoff and lifecycle support.
  • •Experience with big data and cloud/data platforms such as Databricks and AWS, Azure, or Google Cloud Platform.
Experience:10+ yearsAIMachine learningGenerative AILLMsSemantic data systems
Education:Master's
Skills:CommunicationCollaborationAgile developmentCustomer-facing skills
Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnDatabricksSAP DatasphereSAP HANA CloudSAP Business Data CloudSAP One Domain ModelSAP Graph APISAP Business Accelerator HubAWSAzureGoogle Cloud PlatformOWLRDFRDFSSKOSSHACL

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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