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

What you'll do

• Set and lead research directions, test emerging methods, and stop initiatives that lack evidence of impact.
• Develop and evaluate methods for ontology learning, graph-grounded agent retrieval and actions, relational models, and ERP agent benchmarks.
• Partner with product teams, data teams, domain experts, and universities to deploy research results, publish work, and mentor scientists.

What they ask for

• Typically 8+ years in applied research, data science, or machine learning, or a PhD and 5+ years; includes research led that changed a product or platform.
• MS or PhD in computer science, machine learning, statistics, mathematics, or a related field, or equivalent practical experience.
• Deep expertise in one listed area and hands-on experience in another, with at least one in knowledge graphs, ontologies, or semantic data integration.
• Strong machine learning and statistics foundations; fluent in Python and SQL, with PyTorch or JAX; comfortable with schemas spanning thousands of related tables.

Nice to have

• Publications at leading venues or widely used open-source software, datasets, or semantic-standards work.
• Experience in listed areas such as LLM-assisted ontology learning, neuro-symbolic methods, process mining, causal inference, enterprise operations, or mentoring and university collaboration; SAP experience is a plus.

Tools

RDF, OWL, SHACL, SPARQL, Python, SQL, PyTorch, JAX

AI focus

Researches AI methods that learn enterprise business semantics and enable reliable, governed agents, including ontology and knowledge-graph learning, structured-data LLM systems, relational foundation models, and ERP-agent evaluation.