Quantitative Analyst

Overview

As a Quantitative Analyst in the Forecasting & Hedging Team, you will develop and refine forecasting models for import and export portfolios, applying statistical and machine learning methods to boost forecast accuracy and commercial value. You’ll lead code and methodology reviews, deliver automated reporting and dashboards, and support multiple business areas with timely quantitative insights. You will help shape new forecasting capabilities and build scalable data pipelines, staying current with industry developments. This role offers impact in a mission-driven team focused on decarbonising the energy sector.

Pay / Benefits

• discretionary bonus
• Private Healthcare
• SAYE (Sharesave)
• Personal accident cover
• Group personal pension (up to 10%)
• Holiday 25 days plus bank holidays

Responsibilities

• Develop, maintain and enhance forecasting models for import (consumption) and export (renewable generation) portfolios
• Research, evaluate and implement statistical, machine learning and optimisation techniques to improve forecast accuracy and value
• Lead peer reviews of code, model changes and analytical methodologies
• Design and deliver automated reporting, dashboards and visualisations using Python, SQL and Power BI
• Provide ad-hoc quantitative analysis to support Commercial, Renewables, Strategic Sales, Finance and other areas
• Contribute to development of new forecasting capabilities across power demand, renewable generation, weather, pricing and portfolio optimisation
• Build and maintain scalable data processing and automation pipelines for forecasting workflows
• Maintain awareness of industry developments, regulatory changes and emerging analytical techniques in UK energy market

Key requirements

• Strong quantitative and analytical skills with a numerate degree in Mathematics, Statistics, Engineering, Physics, Economics or similar
• Experience (ideally 2+ years) in analytical or energy industry roles and using Python & SQL
• Strong problem-solving skills translating data into practical, commercial solutions
• Excellent communication and stakeholder management to explain complex concepts across teams

• strong communication
• stakeholder management
• problem-solving
• Python
• SQL
• Power BI

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