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For Companies At a Glance
- 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, and opportunities for professional growth.
- Why this job: Make a real impact in the renewable energy sector while using cutting-edge data science techniques.
- Qualifications: Proficiency in Python, experience with time-series modelling, and strong analytical skills.
- Other info: Work in a dynamic startup environment with great career advancement potential.
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.
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.
- 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.
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).
- 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 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
✨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 to make those valuable connections that could lead to job opportunities.
✨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 demonstrate your expertise in Python and data science libraries.
✨Tip Number 3
Tailor your approach! When reaching out to potential employers, mention specific projects or initiatives they’re working on that excite you. This shows you’ve done your homework and are genuinely interested in their mission.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are proactive and passionate about joining our team. Plus, it’s the best way to ensure your application gets noticed by the right people.
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 you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Green Recruitment Company
✨Know Your Data Science Stuff
Make sure you brush up on your Python skills and the data science libraries mentioned in the job description. Be ready to discuss your experience with time-series modelling and optimisation methods, as these are crucial for the role.
✨Understand the Energy Market
Familiarise yourself with electricity markets, PPAs, and forecasting techniques. Showing that you understand the nuances of the energy sector will set you apart from other candidates and demonstrate your genuine interest in the role.
✨Prepare for Technical Questions
Expect technical questions that may involve coding challenges or problem-solving scenarios related to data pipelines and model deployment. Practising these types of questions beforehand can help you feel more confident during the interview.
✨Communicate Clearly
Since you'll be working with non-technical stakeholders, practice explaining complex concepts in simple terms. Being able to translate your analytical outputs into actionable insights will show that you can bridge the gap between data science and business needs.
