Senior Data & Applied Scientist – Ontologies, Knowledge Graphs & AI Research
About The Position
SAP Research & Innovation North America is hiring a senior individual contributor to conduct applied research and develop methods that help AI agents understand and act on enterprise business data. The role focuses on learning and maintaining semantic layers—including ontologies and knowledge graphs—so agents can interpret business objects, relationships, rules, authorizations, and decision history. This is a research and modeling role, not product analytics. The successful candidate will own research threads end to end, moving from open questions and reproducible experiments to prototypes that product and engineering teams can adopt. Areas of work include ontology learning, schema matching and entity resolution; graph-augmented retrieval and text-to-query agents; machine learning on relational, graph, and tabular data; and benchmarks and evaluation methods for realistic enterprise data. The role also contributes reusable Python code, research papers or open-source releases, and mentoring for interns. The team prototypes with dedicated GPU compute using realistic business data and, in co-innovation projects, real data for training and evaluation. It collaborates with universities and research institutes, including Stanford HAI and UC Irvine, and transfers successful research 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.
- An 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 the following areas: 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 or 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 datasets.
- Strong problem-solving skills, demonstrated by turning a vague question into an experiment that produced a clear answer within weeks.
- Strong communication skills, including clearly writing up negative or mixed results and explaining work concisely to scientists or executives, verbally and in a one-pager.
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.
- Familiarity with enterprise processes such as finance, procurement, or supply chain.
- SAP experience.
Responsibilities
- Own research projects from an ambiguous question through method development, benchmarking, reproducible results, and decisions about next steps.
- Reproduce promising research papers on SAP data and recommend which approaches merit further investment.
- Develop methods for learning and maintaining ontologies of business objects, processes, rules, and actions, using SAP semantic models alongside LLMs, schema matching, and entity resolution.
- Research graph-augmented retrieval and text-to-query agents over large schemas, including checks for business rules, access rights, and governed actions.
- Apply graph neural networks and relational foundation models to prediction, anomaly detection, and what-if analysis using knowledge-graph context.
- Build datasets, benchmarks, and evaluation harnesses for realistic enterprise data, using synthetic or privacy-preserving data when real data cannot be shared.
- Develop clean Python code, build prototypes with product and engineering teams, and support adoption of successful work.
- Contribute published research papers or open-source releases and mentor interns.
- Collaborate with universities and research institutes, including Stanford HAI.
Benefits
- SAP describes its benefits as great benefits and provides a link to SAP North America Benefits and eligibility information.
- SAP describes a culture focused on health and well-being and flexible working models.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
