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

What you'll do

• Own research threads from open question through reproducible benchmark and prototype; reproduce promising research and recommend next steps.
• Develop and evaluate methods for ontology learning, graph-grounded agents, and machine learning on relational data using realistic enterprise data.
• Produce reusable Python prototypes, research publications or open-source work, collaborate with product teams, and mentor interns.

What they ask for

• Hands-on data science, machine learning, or applied research experience, with projects taken from an open question to a measurable result; typically 5+ years or a PhD plus 2+ years.
• 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 area: knowledge graphs/ontologies/semantic layers; entity resolution/schema matching/data integration; ML on relational, graph, or tabular data; or LLM systems over structured data.
• Strong ML and statistics foundations, disciplined experimentation, Python and SQL, and PyTorch or a similar framework.

Nice to have

• Publications at relevant research venues or open-source contributions; graph databases, vector search, or large-scale data processing such as Spark.
• LLM-assisted ontology learning, LLM fine-tuning or post-training, MCP, process mining, causal inference, forecasting, or familiarity with enterprise processes.

Tools

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

AI focus

Research and develop ontology-learning methods and AI agents grounded in business context, including graph-augmented retrieval, text-to-query, and models for prediction, anomaly detection, and what-if analysis.