About Asite

We start with a simple idea: the built environment should be smarter, safer and more sustainable. Everything we do is about helping the people behind major construction and infrastructure projects work together more easily and make better decisions.

Asite offers a cloud-based platform that connects project teams, improves collaboration and manages data from the first design to the final handover. Industry leaders such as Laing O’Rourke, Transport for London, MTA New York and Aldar use Asite to keep their projects running smoothly and delivering strong results.

With offices around the world and a record of steady, profitable growth, we are shaping the future of construction technology while supporting the people who build the world around us.

The Role

As an AI Data Scientist at Asite, you will work closely with our AI and Product teams to design, build, and deploy data workflows that support machine learning, analytics, and model development. You’ll help us evaluate and label large volumes of structured and unstructured data, prepare clean datasets, and support the development of new AI capabilities powered by LLMs, embeddings, and vector search.

This is an on-site role in our London office, working alongside a growing team focused on scaling our internal AI capabilities.

What You’ll Be Doing

• Analyse and classify structured and unstructured data from across the platform
• Build clean, reliable datasets and automated ETL workflows
• Develop prototypes in Python using pandas, numpy, scikit-learn, and related libraries
• Support experiments with LLMs, embeddings, and vector search technologies
• Work with cloud services (GCP / AWS / Azure) for model deployment and pipeline automation
• Collaborate with engineers and product teams to integrate data workflows into AI features
• Document processes, tools, and analysis outputs clearly and consistently
• Apply production-quality coding practices including Git, API usage, and modular design

What You Bring

• Strong foundation in statistics, data analysis, and machine learning fundamentals
• Proficiency in Python and common data science tooling
• Experience preparing datasets and building automated pipelines
• Comfortable working with both structured and unstructured data
• Familiarity with cloud platforms (GCP / AWS / Azure)
• Understanding of APIs, Git, and robust coding standards

Nice to Have

• Experience working with LLMs, embeddings, or vector databases
• Exposure to NLP, classification, or document understanding problems
• Experience with data visualisation (Tableau, Power BI, or Python libraries)
• Background in deploying ML models in production environments