Description
Imagine being part of a team that is actively driving the future.
At Davies, we combine technology, analytics, AI, and professional services to help clients solve complex business challenges across insurance and regulated industries.
We are seeking an Ontologist to design, develop, and govern enterprise knowledge models that enable AI, analytics, and intelligent data products.
Key Responsibilities
- Design and maintain enterprise ontologies, taxonomies, business vocabularies, and semantic data models.
- Develop and manage knowledge graphs that connect business entities, relationships, processes, and data assets.
- Collaborate with business and technical teams to define common business concepts and data standards.
- Implement semantic modeling solutions within Microsoft Fabric and related Microsoft data and AI platforms.
- Support generative AI, retrieval augmented generation (RAG), and advanced analytics initiatives through knowledge representation techniques.
- Establish governance processes for ontology lifecycle management, versioning, stewardship, and quality assurance.
- Work with Data Engineering teams to integrate ontologies and graph structures into data pipelines and analytical solutions.
- Document semantic models and provide training to technical and business stakeholders.
- Monitor emerging industry standards and best practices related to knowledge representation, linked data, and semantic technologies.
Skills, Knowledge & Expertise
- Bachelor's degree in Information Science, Computer Science, Data Science, Knowledge Management, Library Science, or related field.
- 3+ years of experience in ontology development, semantic technologies, metadata management, or knowledge graph solutions.
- Experience designing and implementing knowledge graphs and semantic data models.
- Strong understanding of ontology concepts, taxonomy design, and enterprise information architecture.
- Hands-on experience with Microsoft Fabric and modern cloud-based data platforms.
- Experience with graph databases, linked data concepts, RDF, OWL, SPARQL, or similar semantic technologies.
- Strong SQL and data modeling skills with a solid understanding of data engineering principles.
- Knowledge of AI, machine learning, and RAG architectures is highly desirable.
- Excellent communication, documentation, facilitation, and stakeholder management skills.
- Ability to translate complex business concepts into scalable semantic models and knowledge structures.