Data Scientist
At LoweConex, we’re all about empowering sustainable business.
Using Conex OS, our centralised data platform, we make it simple for retailers to observe, analyse and control any building and property asset at scale from one platform.
A multi-award winning closed-based solution, LoweConex works with brands such as Aldi, Stonegate Group, Co-op franchises and Poundland (but to name a few), implementing innovative energy management techniques and cutting-edge automation software applications to drive significant cost savings industry-wide.
Our technology helps keep assets working round the clock, reducing environmental impact, ensuring compliance and maximising profitability for high street brands worldwide.
If you’re passionate about the future of sustainability software, building automation, retail and AI, we may just have the perfect opportunity for you.
Role Overview
A data scientist working at the intersection of smart building technology, IoT, energy optimisation and predictive analytics. Working with rich telemetry from connected building assets, energy data and environmental inputs to deliver intelligent, scalable and automated insights. The work carried out will directly drive operational efficiencies, reduce energy consumption and improve system reliability for customers.
- Perform statistical analysis and deep data interrogation across diverse datasets.
- Develop, test and deploy advanced analytics frameworks that uncover actionable insights.
- Design and implement end to end data science workflows.
- Translate R&D concepts and exploratory analysis into production ready algorithms and reusable components.
- Build machine learning models and design experiments to optimise building performance and asset control strategies.
- Select, apply and evaluate appropriate data science techniques (e.g., regression, classification, clustering, anomaly detection) based on the use case requirements.
- Work closely with internal teams to translate business needs into data led solutions.
- Present complex technical insights in a clear, concise manner to both technical and non technical stakeholders.
- Collaborate with customer success and delivery teams to ensure that models, analytics and insights align with customer needs and expectations.
- Maintain strong internal relationships, contributing to a shared culture of curiosity, continuous improvement and innovation.
- Conduct exploratory research and develop prototypes or proof of concept solutions to test hypotheses or emerging techniques.
- Remain up to date with developments in Artificial Intelligence/Machine learning.
- Contribute to the development of internal best practices, processes and documentation, to increase team effectiveness and reusability of work.
- Evaluate and select the most appropriate data mining models for specific projects
- Communicate complex data insihgts clearly and effectively to both technical and non technical audiences.
- Stay informed and up to date about emerging technologies and methodologies.
- Maintain curiosity and inspiure others about the value of data science.
- Build and maintain relationships.