Lead Data Scientist - Battery Energy Storage

Allye Energy • Full-time • Ruislip, England • 3m ago

Company Description

Allye Energy is building an intelligent energy management platform that transforms how businesses and communities store, share, and optimise electricity. We're creating "energy banking" that combines battery storage with AI-powered software to make clean energy more accessible, affordable, and flexible.

By installing batteries at businesses and alongside legacy grid infrastructure, Allye Energy is building a network of distributed energy storage assets that solve local grid constraints, stabilise networks, and reduce energy costs. Our energy banking model makes energy storage 100 times more affordable for all consumers, providing the flexibility to deposit and withdraw electricity from shared batteries across our network. This revolutionary approach empowers local communities, creates a smarter grid, and accelerates the transition to renewable energy.

Job Description

We are seeking a passionate Data Scientist with a specialisation in linear programming and optimisation of battery energy storage.

This role represents a unique opportunity to work within a rapidly growing and dynamic company. In this role, you will apply quantitative analysis and data science techniques to develop sophisticated models for forecasting and optimisation both behind the meter and in front of the meter for community energy storage. You will lead the development of virtual energy storage, optimising a physical asset based on a digital twin. Your work will directly contribute to the strategic positioning of our energy storage assets in the market, optimizing their financial performance.

This is an exciting role that offers the opportunity to be at the forefront of an emerging industry and make a lasting impact on the future of energy storage.

The ideal candidate will have a background in engineering or computer science either in energy storage, electric vehicles or financial markets and a strong understanding of the energy transition.

Responsibilities

Develop predictive models for market prices, demand, and supply dynamics using machine learning techniques.
Design and implement algorithms for automated trading and real-time decision-making
Perform statistical analysis and back-testing to refine trading strategies and assess their performance.
Monitor and respond to market conditions in real-time, adjusting strategies as necessary to maximize returns.
Communicate complex quantitative analysis and model results to stakeholders in a clear and concise manner.

Qualifications

Must-haves

Advanced Degree (PhD preferred) in Computer Science, Data Science, Financial Engineering, or a related field.
Proven experience in real-time trading and arbitrage modelling, preferably within the energy sector.
Strong proficiency in Python, and experience with other programming languages and statistical tools.
Deep understanding of ancillary services markets and energy trading principles.
Experience with high-frequency data analysis and algorithmic trading systems.
Experience with time-series analysis and forecasting methods.
Must be self-motivated and an effective team player.
Eligible to work in the UK.

Nice-to-haves

An interest in the energy sector and sustainable technologies.
Experience with real-time trading platforms and algorithmic trading.
Experience with cloud computing platforms, such as AWS or GCP, for deploying trading models.
Experience with advanced machine learning techniques, including reinforcement learning and neural networks.