Data Scientist

Overview

In this role, you will develop market forecasts and predictive models to optimise battery storage trading across wholesale, Balancing Mechanism, and ancillary service markets. You’ll collaborate with analytics and trading teams to produce actionable insights that boost revenue and reduce risk, while productionising models and automating trading workflows. You’ll become a subject-matter expert on markets we operate in and help translate data science into real-world trading performance. This is a fast-paced, cross-functional role with a climate-focused mission to maximise asset value and accelerate the energy transition.

Pay / Benefits

• competitive salary
• flexible working arrangements
• personal development opportunities
• hybrid working model
• global team exposure

Responsibilities

• Develop market forecasts for trading teams in GB electricity and ancillary markets
• Build, prototype, test, and scale parallelised predictive models for prices, volumes, and value
• Clean complex datasets and engineer high-value temporal features
• Create actionable insights to improve real-world trading performance and risk management
• Contribute ad hoc insights and enduring intelligence to trading strategies
• Create applications to automate battery trading processes
• Generate insights into risk and outcomes for trading decisions
• Visualise and communicate insights to support high-reward vs. risk decisions
• Become a specialist on market areas and support colleagues across teams
• Collaborate with tech teams to source data and productionise applications

Key requirements

• Time-series modelling (ARIMA, SARIMA)
• Tree-based and gradient boosting models (XGBoost, LightGBM, NGBoost)
• Experience with internal and external data sources
• Python (production-ready, with type hints, linting, etc)
• Dashboard building (Grafana, Streamlit, Superset, Plotly Dash)
• Requirements elicitation, scoping, prototyping, and documentation
• Collaboration with Core/Applied Engineering for productionisation
• Understanding of PnL drivers and energy trade life cycle
• Genuine interest in energy markets and renewables

• Excellent data organisation and storytelling
• Adaptability in a fast-paced trading environment
• Self-starter with strong initiative and multitasking
• Python (production-ready, including pydantic, linting, type hinting)
• Polars, uv, SQLAlchemy, Streamlit
• Postgres, Kubernetes, AWS, Prefect

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