Overview
We are seeking a Data Scientist to strengthen our client’s market and performance analytics capability, supporting commercial strategy and policy decisions across trading and asset operations. The role focuses on extracting insight from market data and asset performance, combining statistical analysis, machine learning, and domain understanding to inform high-impact commercial decisions.
You will work closely with Commercial, Trading, and Analytics teams, translating complex data into clear, decision-ready insight. The role sits at the interface between quantitative analysis and commercial application, complementing optimisation and trading-focused analytics with deeper explanatory and diagnostic capability.
Responsibilities
- Develop and maintain analytical models and dashboards supporting market analysis across wholesale and ancillary services markets; asset performance, value capture, and post-event attribution; and commercial strategy, pricing signals, and policy decision support.
- Apply statistical and machine learning techniques to identify drivers of market behaviour, performance variance, and structural change. Conduct deep-dive analyses on spreads, volatility, regime shifts, and asset utilisation to support strategic and tactical decisions.
- Translate analytical outputs into clear narratives and recommendations for Commercial and senior stakeholders.
- Partner with Trading, Asset Management, and Optimisation teams to ensure consistency of assumptions, data, and interpretation across analytical outputs.
- Contribute to the development of reusable analytics, datasets, and performance metrics within the wider analytics platform.
- Support ad-hoc commercial and policy-driven analysis with rapid, high-quality quantitative insight.
Qualifications
- Strong professional experience as a data scientist or quantitative analyst in a data-intensive environment.
- Advanced Python skills for data analysis, including data manipulation and feature engineering, statistical modelling and machine learning, and clear, reproducible analytical workflows.
- Solid grounding in statistics and applied analytics, including uncertainty, attribution, and performance analysis.
- Experience working with time series data and large, complex real-world datasets.
- Ability to communicate complex quantitative findings clearly to non-technical stakeholders.
- Experience or interest in energy markets, commodities, or infrastructure-heavy industries.
- Familiarity with power market fundamentals, asset economics, or trading-driven performance metrics.
- Experience with modern data stacks, analytical databases, or visualisation tools.
- Exposure to optimisation, forecasting, or simulation models, even if not a primary developer. Comfort operating at the intersection of analytics, commercial decision-making, and policy considerations.
Personal Attributes
- Commercially minded, with a strong instinct for what analysis matters in decision-making.
- Analytically rigorous but pragmatic, balancing precision with timeliness.
- Comfortable working across teams and influencing decisions through insight rather than authority.
- Motivated by applying data science to real-world systems with material financial and operational impact.
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