Senior Data & Applied Scientist – Ontologies, Knowledge Graphs & AI Research
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 complex business data. The role focuses on learning and maintaining semantic layers and ontologies that represent business objects, processes, rules, actions, and decision history. It is a research and modeling position, not product analytics. The scientist will own a research thread end to end, from framing an open question and developing a method to building benchmarks and prototypes that product teams can adopt. Work includes reproducing promising research on SAP data, extending semantic models with techniques such as LLMs, schema matching, and entity resolution, grounding agents with graph-augmented retrieval and text-to-query systems, and applying graph neural networks and relational foundation models to prediction, anomaly detection, and what-if analysis. The role also develops realistic datasets, benchmarks, and evaluation harnesses, using synthetic or privacy-preserving data when needed. The position is based with SAP Research & Innovation North America at SAP Labs in Palo Alto. It is hybrid, with three days a week in the Palo Alto office, and involves travel of up to 10%. The team prototypes with dedicated GPU compute, collaborates with product and engineering teams, universities and research institutes, publishes research or open-source work, and mentors interns. In the first year, success could include taking a research thread from baseline through benchmark to a product-adopted prototype, as well as submitting a paper or releasing open-source work.
Requirements
- Typically 5 or more years of hands-on data science, machine learning, or applied research experience taking projects from open questions to measurable results, or a PhD plus 2 or more years of such 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 and an appetite to learn the others: 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.
- For knowledge graphs, ontologies or semantic layers, experience with technologies such as RDF, OWL, SHACL, SPARQL, or property graphs with Cypher or similar is relevant.
- For LLM systems over structured data, relevant areas include text-to-SQL, schema linking, API discovery, GraphRAG, agents, and evaluation.
- Strong foundations in machine learning and statistics, and 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 communicate negative or mixed results and explain work concisely 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 a research thread from an ambiguous question through method development, 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 using techniques such as LLMs, schema matching, and entity resolution.
- Build graph-augmented retrieval and text-to-query agents 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 necessary.
- Write clean Python and develop prototypes with product and engineering teams for potential adoption.
- Publish research papers or open-source releases.
- Mentor interns.
- Collaborate with universities, institutes, startups, and SAP product teams.
Benefits
- Great benefits; details are referenced through SAP North America Benefits.
- Flexible working models.
- A culture focused on health and well-being.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
