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Allye Energy

Data Science Intern

London, England, GB

RemoteInternshipNo Previous Experience, Early Career

6 months ago

About the Job

Are you a Data Science, Statistics, or Computer Science student, recent graduate or apprentice who has practical hands-on experience with time series forecasting, optimization algorithms and statistical modeling? Do you love working with energy data, building predictive models and solving optimization problems in your spare time? Ideally you've participated in energy forecasting competitions, worked on projects related to demand prediction, battery optimization or linear programming; you want to demonstrate your analytical skills; and want to work in a fast-paced start up environment.

This is a unique opportunity to join a fast-growing London based start-up that is creating an all-new range of battery storage systems for both commercial and consumer markets. You will be responsible for developing forecasting models for energy demand, building optimization algorithms for battery charge/discharge cycles, and creating data pipelines to support our real-time energy management systems.

This internship will give you a unique opportunity to join us at the beginning of our journey, a business where you can contribute, grow and be a success.

About the Company

Allye Energy Logo

Allye Energy

London, England, UK

6-10

Allye provides distributed energy storage at the grid edge to provide collective flexibility to the electricity network, helping accelerate decarbonisation of the grid while lowering energy costs for industrial, commercial and residential customers by up to 50%. Our smartly designed energy storage systems reimagine how batteries are connected, distributed and used. Flexible and modular, our batteries are self-learning, intelligently managed via the cloud to maximise cycle life and arbitrage on electricity prices. Using digital twins, we deploy machine learning and AI to optimise behaviour and performance as a collective of assets, to deliver benefits at an individual level to the end user and the energy network at a system level.