At a Glance
- Tasks: Lead the development of intelligent data systems for construction industry applications.
- Company: Join Depixen, a pioneering tech company transforming construction data.
- 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.
The predicted salary is between 60000 - 80000 £ per year.
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.
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 Manager (Remote) in London employer: Depixen
Depixen is an exceptional employer that fosters a collaborative and innovative work culture, where employees are empowered to tackle complex challenges in the construction industry through cutting-edge technology. With a strong focus on professional growth, team members have access to continuous learning opportunities and the chance to contribute to meaningful projects that shape the future of digital decision-making. Working remotely from London, employees enjoy the flexibility of a modern work environment while being part of a forward-thinking company that values creativity and expertise in data science.
StudySmarter Expert Advice🤫
We think this is how you could land Data Science Manager (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, so make it shine!
✨Tip Number 3
Prepare for interviews by brushing up on relevant topics. Dive deep into machine learning, NLP, and knowledge graphs. Be ready to discuss how you've tackled real-world problems in these areas, as this will set you apart from the competition.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search. So, don’t hesitate—submit your application today!
We think you need these skills to ace Data Science Manager (Remote) in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Data Science Manager role. Highlight your hands-on experience in data science, machine learning, and any relevant projects you've worked on that relate to semantic AI and knowledge graphs.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about this role and how your background makes you a great fit. Be specific about your experience with structured and unstructured data, and mention any relevant technologies or methodologies you've used.
Showcase Your Technical Skills:Don’t shy away from listing your technical skills, especially in Python and any experience with RDF, OWL, or graph databases. We want to see how you can contribute to building intelligent data systems, so make it clear!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team at Depixen!
How to prepare for a job interview at Depixen
✨Know Your Data Inside Out
Before the interview, dive deep into the types of data you'll be working with at Depixen. Familiarise yourself with structured, semi-structured, and unstructured data, as well as knowledge graphs. Being able to discuss specific examples of how you've handled similar data in the past will show your expertise.
✨Showcase Your Technical Skills
Make sure to highlight your Python skills and any experience you have with machine learning models. Be prepared to discuss the tools and frameworks you've used, especially those related to semantic AI and data quality automation. This is your chance to impress them with your technical prowess!
✨Understand the Industry Context
Since this role is focused on the construction industry, it’s crucial to understand the unique challenges and data types involved. Brush up on relevant standards, classifications, and domain knowledge that relate to construction products. This will help you connect your skills to their specific needs.
✨Prepare for Collaborative Scenarios
Collaboration is key in this role, so think about examples where you've worked with cross-functional teams. Be ready to discuss how you’ve turned business requirements into technical solutions, and how you communicate complex ideas to non-technical stakeholders. This will demonstrate your ability to work effectively within their team.
