2026-06-06 –, Hardwick Hub
Decarbonising UK’s home heating is one of the greatest challenges of the Net Zero transition, yet it currently relies on individual household decisions supported by government incentives. To help accelerate the local delivery, we are building a tool that maps the most suitable low-carbon heating for clusters of properties at a neighbourhood level.
In this talk we will walk through our end-to-end data science pipeline, covering processing of large-scale geospatial data, the nuances of modelling where ground truth data does not yet exist, and how to translated local authorities needs into a functional product. We will present our Python tech stack and will conclude with a showcase of the user interface.
Whether you're interested in geospatial data engineering, machine learning for social good, or how to work within a multidisciplinary team, this talk offers a blueprint for building data products with real-world impact.
Decarbonising UK’s home heating is one of the greatest challenges of the Net Zero transition, yet it currently relies on individual household decisions supported by government incentives. To help accelerate the local delivery, we are building a tool that maps the most suitable low-carbon heating for clusters of properties at a neighbourhood level.
We will walk through the end-to-end journey of building a data product, from handling open data (such Ordnance Survey products and EPC) to designing a user interface that empowers non-technical decision-makers.
What we will cover:
- Our data science pipeline: Processing large-scale geospatial data, deployment of classification models and clustering algorithms, evaluating pipelines where ground truth data does not yet exist, etc
- Our Python tech stack
- A walkthrough of the user interface
- The process of translating the needs of local authorities into a functional and intuitive product
Who should attend?
No prior technical knowledge is required. Whether you are a data science newcomer or a seasoned professional with a decade of experience, this talk is designed to be accessible to all. We welcome:
- Data scientist, engineers, academics, machine learning engineers curious about how data science operates within a mission-driven, not-for-profit context.
- Project and product managers looking for a roadmap to steer complex data products from concept to delivery.
Key takeaways:
By the end of this session, you will gain a deeper understanding of:
- Data science in practice: data science techniques and libraries used
- Applying data science for impact: How to bridge the gap between complex modelling and the practical needs of external stakeholders
- Multidisciplinary collaboration: lessons earned from a team of data scientists, full-stack developers, designers, and domain experts working toward a common goal.
Sofia is a principal data scientist at Nesta, working with the sustainable future mission team on decarbonising UK homes. During her time at Nesta, Sofia worked with energy performance certificates, social media and smart meter data to: estimate the cost of low carbon heating technologies, identify issues faced by homeowners in their low carbon heating path, understand how people consume energy in their homes and identifying the most suitable low carbon heating technology for groups of homes.
Prior to joining Nesta, Sofia worked as a data scientist at Imperial College London, assessing the accuracy of crowdsourced data for road traffic collision and injury surveillance. Before this she worked as a research fellow at the Social Physics and Complexity research group, LIP Portugal, on health related projects such as identifying antibiotic over-prescription and factors influencing it.
Sofia holds a Bachelor’s degree in Applied Mathematics and Master’s degree in Data Science and Advanced Analytics.
Simran is a PhD student in high energy physics at University College London, working on direct searches for dark matter with the LUX-ZEPLIN experiment. She is undertaking a data science placement at Nesta, working with the sustainable future team to map the local heat transition using open data. She holds a Master's degree in Theoretical Physics from Imperial College London.