Senior Data and Applied Scientist, Finance and Spend Autonomous Suite, Palo Alto

About the role

The Data and Applied Scientist at SAP will build the semantic and contextual foundation of SAP's AI, transforming raw business data into the knowledge layer that powers SAP's AI agents and assistants.

Responsibilities

• Build and scale the layer that gives SAP's AI agents accurate, grounded understanding of SAP and connected business landscapes.
• Design and maintain enterprise ontologies and semantic models that harmonize data from various sources.
• Develop AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding.
• Leverage SAP's deep data and process context to ground AI solutions in real enterprise reality.
• Work with cloud and data platforms like Databricks, SAP Datasphere, and AWS to support reliable, scalable AI workflows.
• Partner across product, engineering, business, and customer-facing teams to translate business challenges into concrete AI solutions.
• Apply machine learning, deep learning, and statistical modeling to develop and evaluate AI solutions using real-world enterprise datasets.

Requirements

• 5+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science.
• Master's 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 SPARQL, Cypher, or GQL.
• Experience with modern GenAI systems, RAG, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding.
• Strong Python and SQL skills with production-grade development practices; experience with ML libraries like 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-functionally in agile environments.

Preferred Qualifications

• Deep working knowledge of SAP data models, metadata structures, and core business processes.
• 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 and Spend domain knowledge.
• Deep expertise in machine learning and deep learning, with experience developing, evaluating, and improving models on real-world datasets.
• Experience with agentic AI, reasoning frameworks, planning, orchestration, tool use, or multi-agent architectures.
• Experience contributing to reusable AI platforms, foundation model initiatives, or shared AI services adopted across multiple product areas.
• Ability to design upper-level and mid-level ontologies aligned with industry standards and apply semantic interoperability frameworks across complex application landscapes.