Job Summary

What makes SAP's agents different is accuracy grounded in the richest enterprise data and process context in the world.

As a Data and Applied Scientist at SAP, you'll build the context engine grounded in SAP's Business ontology: the semantic infrastructure that transforms raw business data into the knowledge layer powering SAP's AI agents and assistants.

What you'll build

- Design and maintain enterprise ontologies and semantic models that give AI agents accurate, grounded understanding of SAP and connected business landscapes harmonizing data from SAP, Salesforce, Workday, ServiceNow, MES/IoT systems, and external providers into unified semantic layers.

- Build AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding that make SAPs agents accurate and reliable in production.

- Develop AI capabilities including generative AI and LLM-based solutions using enterprise business data, knowledge graphs, business process intelligence, and other structured and unstructured data assets.

- Leverage SAPs deep data and process context including 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 in real enterprise reality.

- Work with cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP to support reliable, scalable AI workflows.

- Partner across product, engineering, business, and customer-facing teams to translate ambiguous business challenges into concrete AI solutions from concept through deployment and continuous improvement.

- Apply machine learning, deep learning, and statistical modeling to develop and evaluate AI solutions using real-world enterprise datasets.

What you'll bring

Required Qualifications

- 5+ years (T3) of experience in knowledge engineering, semantic data systems, applied AI, or data science in industry, research labs,



or advanced academic environments.

- Masters 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); understanding of trade-offs between RDF triple stores and property graph databases.

- Hands-on experience with modern GenAI systems; RAG, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding. (Recommended: Required, per Shardul pending confirmation)

- Strong Python and SQL skills with production-grade development practices;

experience with ML libraries such as PyTorch, TensorFlow, or scikit-learn.

- Proven track record deploying and operating AI/ML solutions in production including handoff, lifecycle support, and continuous improvement.

- Experience with big data infrastructure and cloud environments Databricks or equivalent, plus at least one major cloud (AWS, Azure, or GCP).

- Excellent communication and stakeholder management skills, with the ability to work cross-functional in agile environments.

Preferred Qualifications

- Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end. (SAP knowledge is a solid accelerator)

- Hands-on experience with the SAP data and AI platform stack SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub.

- Deep expertise across the W3C stack (OWL, RDF/RDFS, SKOS, SHACL) and/or property graph query languages (Cypher, GQL).

- Experience on Financial (accounting, close, reporting) and Spend (procurement, s2p, contracts) domain knowledge

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Data Scientist- Finance & Spend, Data Labs (Bengaluru)
🏢 Classic Marble
📍 Bengaluru