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- Tasks: Develop forecasting and optimisation models for a peer-to-peer renewable energy trading platform.
- Company: Join a rapidly scaling company transforming the energy market with innovative technology.
- Benefits: Hybrid work, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact in the renewable energy sector while working with cutting-edge data science.
- Qualifications: Proficiency in Python, experience with time-series modelling, and strong analytical skills.
- Other info: Dynamic startup environment with a focus on collaboration and innovation.
The predicted salary is between 36000 - 60000 £ per year.
Location: London (Hybrid)
About the Company
We are a rapidly scaling peer‑to‑peer Power Purchase Agreement (PPA) platform enabling businesses, generators, and communities to buy and sell renewable electricity directly. Our purpose is to accelerate the shift to a decentralised, transparent, and data‑driven energy system. We build intelligent systems that optimise matching, pricing, and forecasting across distributed generation and consumption. As we expand across the UK and Europe, we are adding strong analytical and modelling capability to our team.
Role Overview
We are hiring a Data Scientist to develop the forecasting, optimisation, and analytical models that underpin our trading platform. You will design, build, and deploy production‑grade models using energy‑market data, asset telemetry, weather data, settlement data, and commercial datasets. This role is central to the evolution of our pricing, risk, trading, and optimisation engines. You will collaborate closely with engineering, product, and commercial teams and work with a high degree of autonomy.
Responsibilities
- Modelling and Forecasting
- Develop time‑series models for generation, consumption, and market price forecasting.
- Build probabilistic and scenario‑based forecasting capabilities.
- Apply machine learning to optimise matching, pairing, and routing algorithms within the P2P marketplace.
- Create algorithms that optimise buyer–seller matching, pricing, and load balancing.
- Support automated PPA structuring, risk scoring, and exposure modelling.
- Develop data‑driven insights to improve trading efficiency and platform performance.
- Work with engineers to design and maintain pipelines for market data, weather feeds, asset data, and settlement information.
- Implement scalable analytics environments and deploy models into production.
- Translate modelling outputs into dashboards, APIs, scoring engines, and product features.
- Provide input into product strategy based on model performance and market trends.
- Communicate insights clearly to non‑technical stakeholders.
- Analyse energy market signals, PPA structures, pricing models, and regulatory factors.
- Develop intelligence around imbalance exposure, generation patterns, demand profiles, and commercial optimisation.
Required Skills and Experience
- Technical Skills
- Proficiency in Python and associated data science libraries (NumPy, Pandas, SciPy, scikit‑learn, PyTorch/TensorFlow).
- Strong experience with time‑series modelling (ARIMA, Prophet, LSTMs or similar).
- Understanding of optimisation methods (linear, mixed‑integer, reinforcement learning desirable).
- Strong SQL and practical experience with production‑ready data pipelines.
- Experience working with cloud environments (AWS, GCP, or Azure).
- Understanding of electricity markets, PPAs, forecasting, imbalance settlement, or asset telemetry.
- Experience with data sources such as system operator data, market pricing feeds, or weather‑driven asset forecasting.
- Ability to work in a fast‑paced startup environment with autonomy and ambiguity.
- Strong communication and problem‑solving skills.
- Ability to convert complex analytical outputs into actionable business recommendations.
- Experience with marketplace or matching algorithms.
- Exposure to flexibility markets, virtual power plants, or reconciliation/settlement processes.
- Experience with ML deployment frameworks (MLflow, Vertex AI, SageMaker).
- Knowledge of optimisation libraries such as Gurobi, OR‑Tools, or Pyomo.
Data Scientist – Peer‑to‑Peer Renewable Energy Trading Platform employer: The Green Recruitment Company
Contact Detail:
The Green Recruitment Company Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist – Peer‑to‑Peer Renewable Energy Trading Platform
✨Tip Number 1
Network like a pro! Get out there and connect with people in the renewable energy sector. Attend industry events, webinars, or even local meetups. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those related to energy markets or predictive modelling. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Don’t just apply blindly! Tailor your approach for each application. Research the company and mention specific projects or values that resonate with you. This shows genuine interest and can make a big difference in how your application is perceived.
✨Tip Number 4
Leverage our website! We’ve got loads of resources and job listings specifically for roles like Data Scientist in the renewable energy space. Make sure to check it out regularly and apply through us for the best chance at landing that dream job!
We think you need these skills to ace Data Scientist – Peer‑to‑Peer Renewable Energy Trading Platform
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role. Highlight your experience with Python, time-series modelling, and any relevant projects that showcase your skills in forecasting and optimisation. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about renewable energy and how your background aligns with our goals. Be sure to mention any experience with energy markets or data-driven insights that could benefit our platform.
Showcase Your Projects: If you've worked on any relevant projects, whether in a professional setting or as personal endeavours, make sure to include them. We love seeing practical applications of your skills, especially those involving machine learning and data pipelines!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at The Green Recruitment Company
✨Know Your Data Science Tools
Make sure you brush up on your Python skills and the libraries mentioned in the job description, like NumPy and Pandas. Be ready to discuss how you've used these tools in past projects, especially for time-series modelling and machine learning.
✨Understand the Energy Market
Familiarise yourself with electricity markets and Power Purchase Agreements (PPAs). Being able to speak knowledgeably about market signals and pricing models will show that you're not just a data whiz but also understand the industry context.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during the interview. Prepare to explain your approach to building forecasting models or optimising algorithms. Practise articulating your thought process clearly, as communication is key in this role.
✨Showcase Your Collaboration Skills
Since this role involves working closely with engineering and product teams, be ready to share examples of how you've successfully collaborated in the past. Highlight your ability to translate complex data insights into actionable recommendations for non-technical stakeholders.
