Data Scientist, SAP

Data Scientist
India, Bangalore
September 29, 2026
Full Time

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Job Description

SAP is hiring a data scientist to develop enterprise AI solutions grounded in business data and semantic context. The role involves building machine learning and generative AI models, building knowledge graphs, working on RAG pipelines, and using statistical modelling. The position demands expertise in Python and SQL programming, team collaboration, and a passion for intelligent enterprise solutions.

Location: Bangalore, India

Qualification: Bachelor’s Degree/Master’s Degree  

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Responsibilities

  • Build and analyse machine learning, deep learning, and statistical models by leveraging enterprise-level datasets.
  • Design generative AI solutions leveraging RAG pipelines, embeddings, vector databases, and semantic search.
  • Enable enterprise-level ontologies, semantic modelling, and knowledge graphs for precise use of AI.
  • Engage in building AI solutions using cloud and data platforms like Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP.
  • Collaborate with the product, engineering, research, and business teams to design AI solutions.

Requirements

  • Bachelor’s or master’s degree in computer science, mathematics, statistics, engineering, or any other quantitative field.
  • At least 3 years of professional experience in computer science, computer engineering, machine learning, or any other relevant field.
  • Basic knowledge of knowledge representation, semantic databases, and graph databases.
  • Knowledge of programming languages such as Python and SQL, and PyTorch, TensorFlow, and scikit-learn.
  • Understanding of generative AI, including RAG, embeddings, vector databases, and semantic search.

Preferred Qualifications

  • Exposure to the design of ontologies, semantics modelling, or knowledge graphs through practical work, research, or internships.
  • Understanding of enterprise software platforms such as SAP, Salesforce, Workday and ServiceNow.
  • Work experience with W3C technologies such as OWL, RDF/RDFS, SKOS, and SHACL.
  • Working experience in machine learning and deep learning models – training, evaluation, and improvement.
  • Inclination towards agentic AI, reasoning, multi-agent systems, planning, orchestration, or reusable codebases.

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