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

SAP Careers • Palo Alto, CA

7d • $198,200 - $420,000 • Hybrid

About The Position

SAP Research & Innovation North America is hiring a principal-level individual contributor to define and lead research on AI for business data, with an emphasis on ontologies, knowledge graphs, and semantic representations. The work aims to give AI agents the business context needed to understand enterprise objects, relationships, rules, authorizations, and decision history so they can act reliably on mission-critical processes. This is a research and modeling role rather than product analytics, combining hands-on experimentation and code with research leadership and product collaboration.

The scientist will set a focused research agenda, identify and evaluate emerging approaches, and develop methods for learning and evolving business ontologies and knowledge graphs from SAP metadata, master data, documents, and process data. Areas include LLM-assisted ontology learning, schema matching and entity resolution, graph-grounded retrieval, semantic parsing into SQL, SPARQL, and OData, governed agent actions, graph and relational foundation models, and benchmarks and simulations for evaluating AI agents on ERP data.

The role works directly with data teams, domain experts, and SAP product teams, including SAP Knowledge Graph, SAP Business Data Cloud, and Joule, to turn research into pilots or shipped product results. It also includes publishing research, releasing code and benchmarks where appropriate, collaborating with universities and research institutes, mentoring scientists and PhD interns, and representing SAP research externally. The position is based in SAP Labs in Palo Alto and is hybrid, with three days per week in the office and travel of up to 10%.

Requirements

• Typically 8 or more years in applied research, data science, or machine learning, or a PhD and 5 or more years of experience, including research leadership that changed a product or platform.
• An MS or PhD in computer science, machine learning, statistics, mathematics, or a related field, or equivalent practical experience.
• Deep expertise in one listed technical area and hands-on experience in another, with at least one area involving knowledge graphs, ontologies, or semantic data integration.
• For knowledge graphs and semantic integration: experience with RDF, OWL, SHACL, SPARQL, or property graphs; ontology and schema matching; entity resolution; or semantic layers, including making substantive modeling decisions.
• For machine learning on structured data: experience with graph neural networks, relational deep learning, or relational or tabular foundation models.
• For LLM systems over structured data: experience with retrieval, tool and API discovery, text-to-SQL and schema linking, tool-using agents, fine-tuning, or post-training.
• Experience with evaluation and benchmark design for AI systems.
• Strong foundations in machine learning and statistics; fluency in Python and SQL; and experience with PyTorch or JAX.
• Ability to work with schemas spanning thousands of related tables.
• Demonstrated problem-solving skills, including turning ambiguous business questions into testable research problems, beginning with simple viable approaches, and stopping or redirecting work based on evidence.
• Strong communication skills, including concise communication of trade-offs to executives, producing written work that influences decisions outside one's team, and working through designs with skeptical engineers.
• Comfort using AI tools, including agentic AI, in day-to-day work, evaluating AI-generated outputs, automating routine tasks, and supporting adoption of AI practices within the team.

Nice To Haves

• Publications at venues such as NeurIPS, ICML, ICLR, KDD, ACL/EMNLP, SIGMOD/VLDB, ISWC, or WWW, or widely used open-source software, datasets, or semantic standards work.
• Experience with LLM-assisted ontology learning, neuro-symbolic methods, agent memory, context graphs and decision traces, or reinforcement learning for tool-using agents.
• Experience with process mining, including object-centric event data, causal inference, forecasting, privacy-preserving data, or synthetic data.
• Familiarity with enterprise operations such as finance, supply chain, procurement, or HR.
• Experience mentoring scientists or leading university collaborations.
• SAP experience.

Responsibilities

• Define a focused research agenda, select research priorities, establish success criteria, and stop or redirect efforts when evidence does not support them.
• Track and evaluate emerging research in areas such as relational and graph foundation models, neuro-symbolic agents, and context graphs.
• Develop methods to learn, align, and evolve business ontologies and knowledge graphs from SAP metadata, master data, documents, and process data.
• Build and investigate graph-grounded retrieval, semantic parsing into SQL, SPARQL, and OData, schema and API discovery, and governed agent actions.
• Research graph learning and relational foundation models that use knowledge-graph structure to predict, explain, and simulate business outcomes.
• Create benchmarks and simulation environments to assess AI agents on ERP data and determine when they are reliable enough to act.
• Collaborate with data teams, domain experts, and co-innovation partners to make enterprise data problems tractable and test methods on realistic business data.
• Work with teams behind SAP Knowledge Graph, SAP Business Data Cloud, and Joule to transition successful research into products or pilots.
• Publish research, release code and benchmarks where appropriate, and co-lead university and institute collaborations.
• Mentor scientists and PhD interns, set standards adopted by other teams, and represent SAP research externally while continuing hands-on coding and experimentation.

Benefits

• SAP describes its benefits as great benefits and refers applicants to SAP North America Benefits for details and eligibility requirements.
• Flexible working models.
• A culture focused on inclusion, health, and well-being.
• Constant learning and skill growth.

What This Job Offers

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