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- Tasks: Develop forecasting and optimisation models for a peer-to-peer renewable energy trading platform.
- Company: Join a rapidly scaling platform transforming the renewable energy market.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Why this job: Make a real impact in the renewable energy sector while honing your data science skills.
- Qualifications: Proficiency in Python, experience with time-series modelling, and strong analytical skills.
- Other info: Work in a dynamic startup environment with a focus on innovation and collaboration.
The predicted salary is between 36000 - 60000 £ per year.
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.
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.
- Trading and Optimisation Intelligence
- 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 trading efficiency and platform performance.
- Data Infrastructure and Engineering
- 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.
- Product and Cross‐Functional Collaboration
- 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.
- Market and Commercial Analytics
- Analyse energy market signals, PPA structures, pricing models, and regulatory factors.
- Develop intelligence around imbalance exposure, generation patterns, demand profiles, and commercial optimisation.
- 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).
- Energy and Market Experience (Highly Desirable)
- 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.
- Professional Skills
- 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.
- Nice to Have
- 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 in London employer: Green Recruitment Company
Contact Detail:
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 in London
✨Tip Number 1
Network like a pro! Get out there and connect with people in the renewable energy and data science sectors. Attend meetups, webinars, or industry events. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to forecasting and optimisation. Use platforms like GitHub to share your code and models. This will give potential employers a taste of what you can do.
✨Tip Number 3
Tailor your approach! When reaching out to companies, including us at StudySmarter, make sure to highlight how your experience aligns with their mission. Mention specific projects or skills that relate to their work in peer-to-peer energy trading.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about joining our team and contributing to a sustainable future.
We think you need these skills to ace Data Scientist – Peer‐to‐Peer Renewable Energy Trading Platform in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match our Data Scientist role. Highlight your proficiency in Python, time-series modelling, and any relevant experience with energy markets. 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 analytical skills can help us optimise our trading platform. Keep it engaging and personal – we love to see your personality!
Showcase Your Projects: If you've worked on relevant projects, whether in a professional or academic setting, make sure to include them. Describe the challenges you faced, the solutions you implemented, and the impact of your work. This helps us understand your hands-on experience!
Apply Through Our Website: We encourage you to apply directly through our website for a smoother application process. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at 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 in 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. Practising coding challenges related to SQL and Python can give you an edge.
✨Communicate Clearly
Since you'll be working with cross-functional teams, practice explaining complex concepts in simple terms. Think of examples where you've successfully communicated insights to non-technical stakeholders, as this will be crucial in your role.
