Senior Data & Applied Scientist – Ontologies, Knowledge Graphs & AI Research

SAP Careers • Palo Alto, CA

6d • $148,000 - $306,300 • Hybrid

About The Position

SAP Research & Innovation North America is hiring a senior individual contributor to research and develop methods that help AI agents understand and act safely on enterprise business data. The role focuses on learning and maintaining semantic layers—including ontologies and knowledge graphs—that represent business objects, processes, rules, actions, relationships, and decision history. SAP semantic models provide a foundation, which the work extends using approaches such as LLMs, schema matching, and entity resolution.

The position owns research threads end to end, from framing open questions through developing methods, establishing benchmarks, and building prototypes that product and engineering teams adopt. Work includes evaluating emerging research on SAP data; grounding agents through graph-augmented retrieval and text-to-query systems; applying graph neural networks and relational foundation models to prediction, anomaly detection, and what-if analysis; and building realistic, privacy-conscious datasets and evaluation tools. Expected outputs may include reusable Python, prototypes, research papers or open-source releases, and mentoring interns. This is a research and modeling role, not product analytics.

The role is based in SAP Research & Innovation North America at SAP Labs in Palo Alto. The team prototypes using dedicated GPU compute and realistic business data, collaborates with universities and institutes, and transfers successful methods to SAP product teams. The position is hybrid, with three days per week in the Palo Alto office, and involves travel of up to 10%.

Requirements

• Typically 5 or more years of hands-on data science, machine learning, or applied research experience taking projects from an open question to a measurable result, or a PhD plus 2 or more years of experience.
• MS or PhD in computer science, machine learning, statistics, mathematics, or a related field, or equivalent practical experience.
• Hands-on depth in at least one of: knowledge graphs, ontologies or semantic layers; entity resolution, schema matching or data integration; machine learning on relational, graph or tabular data, including GNNs; or LLM systems over structured data such as text-to-SQL, schema linking, API discovery, GraphRAG, agents, and evaluation.
• Strong foundations in machine learning and statistics, with disciplined experimentation including strong baselines, ablations, leakage-safe splits, error analysis, and honest reporting.
• Strong Python and SQL skills and experience with PyTorch or a similar framework; comfort working with large, messy, multi-table data.
• Strong problem-solving skills, demonstrated by turning a vague question into an experiment that produces a clear answer within weeks.
• Strong written and verbal communication skills, including the ability to clearly explain negative or mixed results and communicate work to scientists or executives.

Nice To Haves

• Publications at venues such as NeurIPS, ICML, ICLR, KDD, ACL/EMNLP, SIGMOD/VLDB, ISWC, or WWW, or contributions to open-source projects.
• Experience with graph databases, vector search, or large-scale data processing such as Spark.
• Experience with LLM-assisted ontology learning or knowledge-graph construction.
• Experience with LLM fine-tuning or post-training, or MCP.
• Experience with process mining, causal inference, or forecasting, and familiarity with enterprise processes such as finance, procurement, or supply chain.
• SAP experience.

Responsibilities

• Own research threads from ambiguous questions through methods, experiments, reproducible results, and decisions about next steps.
• Reproduce promising research papers on SAP data and recommend which approaches merit further investment.
• Develop methods to learn and maintain ontologies of business objects, processes, rules, and actions, extending SAP semantic models with techniques such as LLMs, schema matching, and entity resolution.
• Develop agents grounded in business context, including graph-augmented retrieval and text-to-query systems over large schemas, with checks for business rules, access rights, and governed actions.
• Apply graph neural networks and relational foundation models with knowledge-graph context to prediction, anomaly detection, and what-if analysis.
• Create datasets, benchmarks, and evaluation harnesses for realistic enterprise data, using synthetic or privacy-preserving data where real data cannot be shared.
• Write clean Python and build prototypes with product and engineering teams for potential adoption.
• Publish research papers or open-source releases and mentor interns.
• Collaborate with universities and research institutes, including Stanford HAI.
• In the first year, own a hands-on research thread through a benchmark and a product-team-adopted prototype, and work toward a paper submission or open-source release.

Benefits

• Great benefits (details and eligibility requirements are referenced on SAP North America Benefits).
• Flexible working models.
• Health and well-being support.
• Constant learning and skill growth.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior