Our mission at Cloud Cycle is to reduce global CO2 emissions by 1% and save the construction industry billions of pounds in the process. We are making this huge impact by helping concrete suppliers deliver better quality concrete with much less waste.
At Cloud Cycle we are pioneering a data-driven technology platform that provides key insights to enable personnel in the global concrete supply chain to make informed decisions that drive quality and efficiency.
We are approaching our second large funding round and have won several large government grants to support our product roadmap and R&D pipeline.
We are scaling rapidly and we need capable, creative, passionate, and motivated individuals to be a part of the Cloud Cycle mission.
In the first six months you will
- Analyse the data from our on-truck measurement system
- Process and visualise large datasets suitable for presentation to our customers
- Implement first principle and machine learning based methods
- Support the backend team in deploying to AWS
- Build dashboards and visualise data using QuickSight
- Apply signal processing techniques to reduce noise and improve algorithm performance
Required Skills
- Degree in Computer Science, Engineering, Mathematics or equivalent experience
- Master’s in data science or other computational science
- 3+ years of Python programming experience with focus on data manipulation and analysis libraries like Pandas
- 3+ years working with large datasets performing clustering, classification, and regression machine learning or deep learning
- Excellent documentation and data presentation skills
- Experience communicating highly technical results to a diverse audience
- A desire to work in a fast-paced startup environment
Nice to Have
- Experience solving problems using Digital Signal Processing
- Experience with the properties of fresh concrete
- Use of AWS Lambda functions and Athena
- Experience working with time series data generated by IoT platforms
Package
Salary for this role is in the range of £60-80k plus an equity component dependent on experience.
We’re open to flexible, hybrid working.
