At a Glance
- Tasks: Build intelligent data systems for classification, enrichment, and semantic search in construction.
- Company: Join Depixen, a pioneering tech company transforming the construction industry.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovative AI solutions.
- Why this job: Make a real impact by turning fragmented data into reliable decision intelligence.
- Qualifications: Bachelor’s degree in relevant field and 4+ years of data science experience required.
The predicted salary is between 60000 - 80000 £ per year.
Semantic AI & Knowledge Graphs
About Depixen
Depixen is a London-based technology company building the digital decision infrastructure of the construction industry. As a corporate member of the World Wide Web Consortium — W3C, Depixen develops Linked Data architectures, domain-specific taxonomies, ontologies, RDF-based data structures, and knowledge graph infrastructures for construction, architecture, and building products. We work with fragmented and highly contextual industry data: product catalogues, technical documents, standards, materials, suppliers, projects, events, images, and user interactions.
About the Role
We are looking for a Senior Data Scientist to help build intelligent data systems for classification, enrichment, entity resolution, semantic search, recommendation, and knowledge graph-connected AI applications. This is not a conventional data science role. You will work at the intersection of machine learning, semantic data modelling, information retrieval, knowledge graphs, and real-world construction product data. Your work will help turn fragmented industry data into reliable, contextual, and explainable decision intelligence.
Responsibilities
- Develop machine learning and data science systems for classification, extraction, enrichment, matching, recommendation, and semantic search.
- Work with structured, semi-structured, unstructured, and graph-connected data.
- Build entity extraction, entity resolution, deduplication, and similarity-matching workflows.
- Connect AI outputs with taxonomy, ontology, RDF, and knowledge graph layers.
- Design evaluation, benchmarking, validation, and error-analysis processes.
- Improve data quality, consistency, explainability, provenance, and semantic alignment.
- Collaborate with engineering, product, and domain teams to turn business requirements into scalable technical systems.
- Support the development of production-grade semantic AI and decision-support systems.
Required Qualifications
- Bachelor’s degree in Computer Science, Data Science, AI, Software Engineering, Mathematics, Statistics, or a related field.
- 4+ years of hands-on experience in data science, machine learning, information retrieval, NLP, knowledge graphs, or related AI/data fields.
- Strong Python skills.
- Experience developing, testing, deploying, and monitoring data science or machine learning models.
- Experience with structured, semi-structured, and unstructured data.
- Practical experience in several of the following areas:
- classification
- entity extraction
- entity resolution
- semantic enrichment
- recommendation systems
- semantic search
- information retrieval
- NLP
- data quality automation
- Strong understanding of data modelling, metadata, data quality, model evaluation, benchmarking, and error analysis.
- Ability to document technical work clearly and communicate across technical and non-technical teams.
- Interest in semantic web technologies, knowledge graphs, ontologies, taxonomies, or linked data.
Preferred Qualifications
- Master’s or PhD in Computer Science, AI, Data Science, Machine Learning, NLP, Semantic Web, Knowledge Graphs, or a related field.
- Experience with RDF, OWL, SPARQL, SHACL, SKOS, JSON-LD, schema.org, ontologies, taxonomies, or Linked Data.
- Experience with graph databases or triple stores such as GraphDB, Stardog, Neo4j, Amazon Neptune, or Blazegraph.
- Experience with embeddings, vector databases, RAG, LLM-based enrichment, or knowledge graph completion.
- Experience with MLOps tools such as Docker, Kubernetes, MLflow, Weights & Biases, Airflow, or similar.
- Experience with AWS, GCP, or Azure.
- Experience in construction, architecture, BIM, building materials, technical product data, catalogues, standards, or compliance-heavy data systems.
- Contributions to open-source projects, academic publications, or applied research are a plus.
Problem Areas
You may work on: construction product classification and enrichment; entity resolution for products, suppliers, events, venues, organizations, and technical concepts; semantic search and recommendation systems; extraction of structured data from catalogues, PDFs, websites, and technical documents; knowledge graph-connected AI workflows; data quality, explainability, provenance, and validation systems.
Why This Role Is Different
Construction data cannot be understood through statistical patterns alone. The meaning of a product, material, document, technical value, supplier, or project depends on its relationship to standards, classifications, specifications, and domain knowledge. At Depixen, data science outputs are not isolated predictions. They are connected to verified data, semantic classification, ontology, RDF, and knowledge graph layers. This role is about building reliable, contextual, explainable, and machine-interpretable AI systems for one of the world’s most complex industries.
Data Science Data Science Data Modeler (Remote) in London employer: Depixen
Depixen is an exceptional employer that fosters a collaborative and innovative work culture, where employees are empowered to develop cutting-edge data science solutions for the construction industry. With a strong focus on employee growth, we offer opportunities for continuous learning and professional development, alongside the unique advantage of working remotely from London while contributing to meaningful projects that transform fragmented industry data into reliable decision intelligence.
StudySmarter Expert Advice🤫
We think this is how you could land Data Science Data Science Data Modeler (Remote) in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend virtual meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to data science and machine learning. This is your chance to demonstrate your expertise in semantic AI and knowledge graphs.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical teams.
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Tailor your application to highlight how your experience aligns with the role and our mission.
We think you need these skills to ace Data Science Data Science Data Modeler (Remote) in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Data Science Data Modeler. Highlight your experience with machine learning, semantic data modelling, and knowledge graphs. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about working at Depixen and how your background makes you a perfect fit for this unique role. Let us know what excites you about the intersection of AI and construction data.
Showcase Relevant Projects:If you've worked on projects involving classification, entity resolution, or semantic search, make sure to showcase them. We love seeing practical examples of your work that demonstrate your expertise in handling complex data systems.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our company and culture.
How to prepare for a job interview at Depixen
✨Know Your Data Inside Out
Make sure you’re well-versed in the types of data you'll be working with, especially structured, semi-structured, and unstructured data. Brush up on your knowledge of RDF, ontologies, and knowledge graphs, as these are crucial for the role.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and any relevant machine learning frameworks. Bring examples of projects where you've developed, tested, or deployed models, and be ready to explain your thought process and the challenges you faced.
✨Understand the Construction Industry
Familiarise yourself with the construction industry’s specific data needs and challenges. Knowing how data classification, enrichment, and semantic search apply to this field will set you apart and show your genuine interest in the role.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll need to collaborate with both technical and non-technical teams, so being able to articulate your ideas clearly is key to success in this role.
