Power Analyst – Data Scientist

When you join EDF Trading, you’ll become part of a diverse international team of experts who challenge conventional ideas, test new approaches, and think outside the box.

Energy markets evolve rapidly, so our team needs to remain agile, flexible, and ready to spot opportunities across all the markets we trade in power, gas, LNG, LPG, oil, and environmental products.

EDF Group and our customers all over the world trust that their assets are managed by us in the most effective and efficient manner and are protected through expert risk management. Trading for over 20 years, it’s experience that makes us leaders in the field. Energy is what we do.

Become part of the team and you will be offered a great range of benefits, which include (location dependent) hybrid working, a personal pension plan, private medical and dental insurance, bi-annual health assessments, corporate gym memberships, an electric car lease programme, childcare vouchers, a cycle-to-work scheme, season ticket loans, volunteering opportunities, and much more.

Gender balance and inclusion are very high on the agenda at EDF Trading, so you will become part of an ever-diversifying family of around 750 colleagues based in London, Paris, Singapore, and Houston. Regular social and networking events, both physical and virtual, will ensure that you always feel connected to your colleagues and the business.

Who are we?

We are EDF Trading, part of the EDF Group - a world leader in low-carbon, sustainable electricity generation.

Join us, make a difference, and help shape the future of energy.

Job Description:

Department

Energy Market Analysis (Power Analytics)

Position purpose

The purpose of the team is to provide a view on the fundamentals of the European power system – the balance of supply and demand – convert them into numerical price forecasts and advise traders on the positions to take based on such forecasts.

The purpose of the position is to improve the scope of these forecasts and ultimately the profitability of trades.

Main Responsibilities

• Primary responsibility will be to develop other time-series forecasting models:
• initially taking over existing embryonic models for meteo-to-power (mainly wind and solar) and making them more precise and industrial;
• continuously adding features as renewables become more integrated in the power system;
• with time, moving on to other time-series;
• making sure forecasts are error-free and delivered to traders in a timely manner.
• Secondary responsibility will be helping with the day-to-day running of the team. Tasks will include:
• data collection/market intelligence;
• liaising with Data in setting up new feeds and monitoring the quality of existing ones;
• running forecast models; automating them as needed;
• maintaining dashboards and reports;
• proactively providing modelling advice to other analysts and stakeholders.
• Developing an in-depth understanding of power fundamentals

Experience required

• Some experience of working in the energy market or a trading environment would be beneficial but not essential.
• Proven academic excellence in the field.
• A couple years experience with building production-grade models essential (MLOps).

Technical requirements

• Advanced graduate qualification (PhD or postdoctorate) in Machine Learning, Data Science, Statistics or Maths.
• Strong Python coding experience/knowledge essential. Knowledge of git and CI/CD practices. Demonstrable proficiency with packages such as Pandas, Sklearn; Pytorch or Keras.
• Knowledge of SQL and Excel; experience with Databricks would be welcome.

Person specification

• Excellent analytical skills and attention to detail.
• Works effectively in a fast-paced environment, where the market and business needs can be continuously evolving.
• Is a team-player: collaborates well with others and is comfortable communicating with senior stakeholders on technical and project delivery details.
• Willingness to learn and incorporate the often very technical features of power fundamentals.

Hours of work:

40 hours per week

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