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

SAP•Palo Alto, CA

9d•Hybrid

About The Position

The context engine that makes AI enterprise ready. Anyone can build an AI agent. 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 help 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. This is an early-career role for engineers and scientists who are sharp, curious, and ready to do real work on hard problems from day one.

Requirements

Bachelor's or Master's in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field recent graduates welcome.
2+ years of CS, CE, ML or related field experience work
Foundational understanding of knowledge representation, semantic data systems, or graph databases (through coursework, research, or personal projects).
Familiarity with at least one graph query language (SPARQL, Cypher, or GQL) or a willingness to learn quickly; some exposure to the trade-offs between RDF triple stores and property graph databases is a plus.
Exposure to modern GenAI concepts — RAG, embeddings, vector databases, semantic retrieval — through coursework, research, or hands-on experimentation.
Solid Python and SQL skills; some experience with ML libraries such as PyTorch, TensorFlow, or scikit-learn (academic projects, research work, and personal projects all count).
Eagerness to learn production-grade development practices and grow into operating AI/ML solutions end-to-end.
Clear, collaborative communication style — you ask good questions, explain your thinking, and work well with others.

Nice To Haves

Hands-on experience — through internships, research, or projects — with ontology design, semantic modeling, or knowledge graphs.
Any exposure to enterprise software ecosystems (SAP, Salesforce, Workday, ServiceNow, or similar) is a real accelerator here.
Familiarity with the W3C stack (OWL, RDF/RDFS, SKOS, SHACL) or property graph query languages (Cypher, GQL).
Academic or project experience in machine learning and deep learning, including training, evaluating, and improving models on real datasets.
Curiosity about agentic AI, reasoning frameworks, multi-agent architectures, or planning and orchestration.
Experience contributing to shared or reusable codebases — open-source projects, research codebases, or team projects.

Responsibilities

Support the design and maintenance of enterprise ontologies and semantic models that give AI agents accurate, grounded understanding of SAP and connected business landscapes — learning how data from SAP, Salesforce, Workday, ServiceNow, MES/IoT systems, and external providers gets harmonized into unified semantic layers.
Contribute to AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding that make SAP's agents accurate and reliable in production.
Develop and iterate on AI solutions — including generative AI and LLM-based approaches — using enterprise business data, knowledge graphs, business process intelligence, and structured and unstructured data assets.
Learn SAP's deep data and process context — data models, metadata structures, and business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce — and apply that context to ground AI solutions in real enterprise reality.
Work with modern cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP, gaining hands-on experience with scalable AI workflows.
Collaborate across product, engineering, and business teams to understand how ambiguous business challenges get translated into concrete AI solutions, and contribute meaningfully to that process from early stages through deployment.
Apply machine learning, deep learning, and statistical modeling to build and evaluate AI solutions using real-world enterprise datasets.