Data Science Chief Expert - Finance (Bangalore, IN, 560066)

SAP
Bengaluru
Workplace: HybridFull timeFunction: Data Science & Machine LearningExperience: 10+ yearsEducation: mastersSkills: ["Communication","Collaboration","Analytical thinking","Customer-facing mindset","Curiosity"]

Build and scale SAP’s context engine for enterprise-ready AI agents, grounded in SAP Business ontology and semantic models. Leverage deep SAP data and process understanding across core business flows to design ontologies, knowledge graphs, and semantic data solutions. Develop end-to-end ML/AI systems—from data preprocessing and feature engineering through deployment, production handoff, and lifecycle support—using cloud and data platforms. Partner cross-functionally to deliver scalable, production-ready outcomes.

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FursaFursa
SAP
SAP
2 hours ago

Data Science Chief Expert - Finance (Bangalore, IN, 560066)

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Last checked: 2 hours agoStatus: Live

Job Summary

Build and scale SAP’s context engine for enterprise-ready AI agents, grounded in SAP Business ontology and semantic models. Leverage deep SAP data and process understanding across core business flows to design ontologies, knowledge graphs, and semantic data solutions. Develop end-to-end ML/AI systems—from data preprocessing and feature engineering through deployment, production handoff, and lifecycle support—using cloud and data platforms. Partner cross-functionally to deliver scalable, production-ready outcomes.
Location: Bengaluru
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Build and scale the semantic and contextual foundation for SAP’s AI agents, grounded in SAP Business ontology.
  • •Leverage SAP data models and end-to-end process semantics to design AI and semantic data solutions across 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 sources.
  • •Develop, evaluate, and operationalize end-to-end ML/AI solutions, including experimentation, validation, deployment, production handoff, and continuous improvement.
  • •Partner with product, engineering, business, and customer-facing teams to ensure solutions are scalable, practical, and production-ready.
Travel: Low travel

Key Requirements

  • •Master's degree or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field.
  • •10+ years of experience in machine learning, deep learning, statistical modeling, generative AI, and LLMs, with hands-on development and improvement using real-world datasets.
  • •Strong Python and SQL skills, including production-grade Python development and experience with PyTorch, TensorFlow, and scikit-learn.
  • •Proven experience deploying, shipping, and operating ML/AI solutions in production, including handoff and lifecycle support.
  • •Experience with enterprise data/semantic systems and SAP data platform stack, including SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub.
Experience:10+ yearsAIMachine learningDeep learningGenerative AIKnowledge graphs
Education:Master's
Skills:CommunicationCollaborationAnalytical thinkingCustomer-facing mindsetCuriosity
Tech Stack:PythonSQLPyTorchTensorFlowScikit-learnDatabricksAWSAzureGoogle Cloud PlatformSAP DatasphereSAP HANA Cloud Knowledge Graph EngineSAP Business Data CloudSAP One Domain ModelSAP Graph APISAP Business Accelerator HubSalesforceWorkdayServiceNowMES/IoTOWL

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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