At a Glance
- Tasks: Develop and enhance forecasting models for energy markets using machine learning.
- Company: Join a leading energy trading organisation with a dynamic Analytics team.
- Benefits: Competitive salary, professional development, and exposure to real-world energy markets.
- Other info: Collaborative environment with opportunities to work alongside industry experts.
- Why this job: Make a direct impact on trading decisions while solving complex problems.
- Qualifications: PhD in a quantitative field and experience in data science or machine learning.
The predicted salary is between 45000 - 55000 £ per year.
We're partnering with a leading energy trading organisation to recruit a talented
Data Scientist to join its growing Analytics team.
This is an exciting opportunity for an early-career Data Scientist with a strong academic background and a passion for applying machine learning to real-world energy markets.
Working alongside experienced Data Scientists, Quantitative Analysts and Traders, you'll help develop forecasting models that support trading decisions across power, gas and other energy markets.
If you're looking to combine advanced analytics with a fast-paced commercial environment, this role offers the chance to work on challenging problems with direct business impact.
The Role
As a Data Scientist, you'll contribute to the development and enhancement of forecasting models using machine learning and statistical techniques.
You'll analyse complex datasets, identify market signals and help deliver predictive insights that support Front Office trading activity.
You'll work collaboratively across Trading, Quantitative Analytics and Technology teams, gaining exposure to both the technical and commercial aspects of energy trading.
Key Responsibilities
- Develop and improve forecasting models for energy markets, including power and gas.
- Apply machine learning and statistical techniques to predict prices, demand and other key market variables.
- Analyse large datasets from market, weather, generation and trading sources to identify predictive patterns.
- Support the design, testing and validation of forecasting models.
- Build and maintain data pipelines to support model development and deployment.
- Collaborate with Traders and Quantitative Analysts to understand business requirements and translate them into analytical solutions.
- Monitor model performance and recommend enhancements.
- Present findings and insights to both technical and non-technical stakeholders.
- Keep up to date with developments in machine learning and forecasting methodologies.
About You
We're looking for someone with a strong quantitative background who enjoys solving complex problems and wants to build a career in energy trading analytics.
You’ll ideally have
- A Ph D in Mathematics, Statistics, Physics, Computer Science, Machine Learning, Data Science or another highly quantitative discipline.
- 1–3 years' commercial experience as a Data Scientist, Machine Learning Engineer or Quantitative Analyst.
- Experience developing forecasting or predictive models using machine learning techniques.
- Strong Python programming skills and experience with common data science libraries such as Pandas, Num Py and Scikit-learn.
- Experience working with SQL and large datasets.
- A solid understanding of statistics, time-series analysis and predictive modelling.
- Strong problem-solving and analytical skills.
- Excellent communication skills and the ability to explain technical concepts clearly.
- Exposure to energy markets, commodities or financial markets.
- Experience with time-series forecasting techniques.
- Knowledge of cloud platforms or distributed computing.
- Familiarity with Tensor Flow, Py Torch or similar machine learning frameworks.
- Experience using Git and software development best practices.
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Data Scientist - Energy Trading employer: Bonhill Partners
Bonhill Partners offers an exceptional work environment for Data Engineers, providing the opportunity to engage with cutting-edge technologies in a dynamic global commodities trading setting. Employees benefit from a culture of innovation and collaboration, alongside ample opportunities for professional growth and development. Located in a vibrant industry hub, the company fosters a supportive atmosphere that values creativity and encourages team members to push the boundaries of data engineering.
