Principal Data and Applied Science, Finance and Spend Autonomous Suite, Palo Alto

About the role

The Data and Applied Scientist at SAP builds the semantic and contextual foundation of SAP's AI, transforming raw business data into the knowledge layer powering SAP's AI agents and assistants. This role requires a deep understanding of enterprise data and process context, leveraging SAP's rich data models and AI platforms.

Responsibilities

• Build and scale enterprise ontologies and semantic models grounded in SAP's Business ontology.
• Develop AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding.
• Apply machine learning, deep learning, and statistical modeling to develop and evaluate AI solutions using real-world enterprise datasets.
• Work with cloud and data platforms like 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 business challenges into concrete AI solutions.

Requirements

• 8+ 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.
• Strong Python and SQL skills with production-grade development practices; experience with ML libraries like PyTorch, TensorFlow, or scikit-learn.
• Experience with big data infrastructure and cloud environments (Databricks, AWS, Azure, 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 SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub.
• Expertise across the W3C stack (OWL, RDF/RDFS, SKOS, SHACL) and/or property graph query languages (Cypher, GQL).
• Domain knowledge in Financial and Spend areas.
• Experience with machine learning and deep learning, 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.
• Ability to design upper-level and mid-level ontologies aligned with industry standards and apply semantic interoperability frameworks.