At a Glance
- Tasks: Transform complex data into intelligent knowledge ecosystems and build scalable Knowledge Graphs.
- Company: Join a forward-thinking Data & AI team at the forefront of innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborate with industry leaders on cutting-edge AI and Knowledge Graph initiatives.
- Why this job: Make a real impact by driving enterprise-wide data interoperability and innovation.
- Qualifications: 5+ years in Ontology Engineering and Knowledge Modeling with strong technical expertise.
The predicted salary is between 70000 - 90000 £ per year.
Are you passionate about transforming complex enterprise data into intelligent, connected knowledge ecosystems? We're looking for an experienced Ontologist / Knowledge Modeler to join our growing Data & AI team and help build the semantic foundation powering next-generation AI, Knowledge Graphs, Data Discovery, and Intelligent Search solutions. This is an exciting opportunity to work at the intersection of Knowledge Engineering, Semantic Technologies, Linked Data, Knowledge Graphs, and Artificial Intelligence, driving enterprise-wide data interoperability and innovation.
About the Role
As an Ontologist / Knowledge Modeler, you will lead the design and implementation of enterprise knowledge models, ontologies, taxonomies, and semantic architectures that enable organizations to unlock the full value of their data. You will work closely with Data Architects, AI/ML Engineers, Product Teams, and Business Stakeholders to create scalable knowledge frameworks that support advanced analytics, AI-driven insights, semantic search, and intelligent decision-making.
What You'll Be Working On
- Knowledge Modeling & Ontology Engineering
- Design, develop, and maintain enterprise ontologies, taxonomies, and semantic data models
- Build scalable Knowledge Graphs that connect and contextualize data across multiple systems and domains
- Develop linked-data architectures using semantic web technologies including RDF, OWL, SKOS, SPARQL
- Align enterprise data models with industry standards such as BIAN (Banking Industry Architecture Network) and MISMO (Mortgage Industry Standards Maintenance Organization)
- Create reusable metadata schemas, vocabularies, and semantic frameworks to improve data consistency and discoverability
- Knowledge Graph & Semantic Platform Development
- Design and implement enterprise Knowledge Graph solutions using technologies such as Neo4j, ArangoDB, GraphDB, JanusGraph
- Develop semantic search and contextual retrieval capabilities
- Enable intelligent data discovery, entity resolution, and AI-powered recommendations
- Leverage graph analytics and SPARQL endpoints to generate actionable business insights
- AI & Advanced Analytics Enablement
- Partner with AI and Data Science teams to integrate Knowledge Graphs into AI and Machine Learning workflows
- Support NLP, contextual search, retrieval systems, and intelligent knowledge management initiatives
- Drive innovation by applying semantic technologies to enhance enterprise AI capabilities
What We're Looking For
Required Experience
- 5+ years of experience in:
- Ontology Engineering
- Knowledge Modeling
- Semantic Technologies
- Knowledge Graph Development
- Proven experience working with industry frameworks such as:
- BIAN
- MISMO
- Experience building enterprise semantic models and data interoperability solutions
Technical Expertise
- Strong hands-on experience with:
- RDF
- OWL
- SPARQL
- SKOS
- Knowledge Graph Platforms:
- Neo4j
- ArangoDB
- GraphDB
- JanusGraph
- TopBraid Composer
- RDF4J
- Graphistry
- Gephi
- Programming experience in:
- Python
- Java
- Experience integrating graph-based solutions with enterprise applications and data platforms
- Mortgage Industry Standards
- Regulatory Compliance Frameworks
- Healthcare Data Standards (Nice to Have)
What Makes You Successful
- Strong analytical and conceptual thinking skills
- Ability to translate complex business concepts into semantic models
- Passion for emerging technologies, AI, and Knowledge Engineering
- Ability to work independently while collaborating effectively across cross-functional teams
- Detail-oriented with a strong focus on quality and scalability
Education & Certifications
- Bachelor's or Master's Degree in Information Science, Data Science, Knowledge Management, Computer Science, or Related Discipline
- Certifications in Ontology Engineering, Knowledge Management, Semantic Technologies, or Data Standards are highly valued.
If you're passionate about Knowledge Graphs, Ontologies, Semantic AI, Linked Data, and Enterprise Knowledge Management, we'd love to connect with you.
Ontologist / Knowledge Modeler in London employer: Mphasis
Join our innovative Data & AI team as an Ontologist / Knowledge Modeler, where you'll have the opportunity to shape the future of enterprise data through cutting-edge semantic technologies and Knowledge Graphs. We pride ourselves on fostering a collaborative work culture that encourages continuous learning and professional growth, offering access to the latest tools and frameworks in AI and data science. Located in a vibrant tech hub, we provide a dynamic environment that not only values your expertise but also supports your career aspirations with exciting projects and industry-leading teams.
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We think this is how you could land Ontologist / Knowledge Modeler in London
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We think you need these skills to ace Ontologist / Knowledge Modeler in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
