Data Scientist jobs in United Kingdom

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Short Term Intraday Stack Models

Power Trading

London

We are working with one of the UK's leading power companies - generating 10-15% of the UK's electricity - with sites across the UK, Ireland, and Germany. As they scale from 3.5 GW to 7 GW of and add 500 MW of battery storage, they're building out their data science capability on the trading floor.

We are seeking a highly motivated Quantitative Power Analyst to join our trading team. This role is focused on short-term power markets and sits at the intersection of trading, quantitative analysis, and data-driven modelling.

1. Developing algorithms for asset optimisation in short-term power markets.
2. Applying short-term power trading expertise to drive trading desk performance.
3. Building and improving short-term pricing models (e.g., stack models, scratch models).

• Take ownership over existing models, and build new short-term intraday stack models.
• Own and enhance short-term stack forecasting and dispatch models.
• Develop algorithms for optimising assets in the short-term power market, including stack/dispatch models used for pricing and trading decisions.
• Work directly with the trading desk to support short-term power trading strategies and decisions, ensuring models are commercially relevant and impactful.
• Build intraday trading tools and collaborate on automated strategies.
• Apply time series and fundamentals-based modelling to support trading decisions.
• Work alongside data engineers to deploy production-grade code.
• Mentor others and help embed data science best practices across the team.

• Strong quantitative background; Master’s degree in computer science, mathematics, engineering, physics, machine learning, or a related field. Ph.D. is a plus.
• Proficiency in Python and ability to write clean, production-quality code.
• 5+ years of relevant experience in short-term power trading, quant analysis, or algorithmic modelling.
• Strong experience in short-term power trading, with direct impact on trading desk decisions.
• Hands‑on expertise in developing algorithms for asset optimisation in the short‑term power market.
• Proven experience with stack/dispatch modelling and short‑term pricing techniques.
• Experience in power markets, with knowledge of financial markets and trading concepts.
• Experience with back‑testing techniques appropriate to financial market applications.
• Experience exploring and extracting insights from heterogeneous multi‑dimensional data sets, and presenting complex data visually.
• Time series modelling (both machine‑learning and econometric approaches).

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