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An established industry player is seeking a Data Analyst to enhance forecasting and modeling efforts across renewable and conventional energy sectors. This role involves managing large datasets and delivering actionable insights in collaboration with cross-functional teams. The ideal candidate will possess strong technical skills in SQL and Python, along with a passion for data analysis and problem-solving. Join a forward-thinking company that values innovation and continuous learning, and contribute to improving global asset performance and revenue strategies in a dynamic environment.
Wood Mackenzie is the global data and analytics business for the renewables, energy, and natural resources industries. Enhanced by technology. Enriched by human intelligence. In an ever-changing world, companies and governments need reliable and actionable insight to lead the transition to a sustainable future. That’s why we cover the entire supply chain with unparalleled breadth and depth, backed by over 50 years’ experience. Our team of over 2,400 experts, operating across 30 global locations, are enabling customers’ decisions through real-time analytics, consultancy, events and thought leadership. Together, we deliver the insight they need to separate risk from opportunity and make confident decisions when it matters most.
Role Purpose
The Data Analyst will play a key role in advancing forecasting and modelling efforts by managing, analysing, and optimising large datasets across renewable, conventional, and storage assets globally. Working on asset revenue forecasting, power dispatch modelling, or power markets data, the analyst will focus on automating workflows, ensuring data quality, and delivering actionable insights through close collaboration with research, engineering, and product teams. This role contributes directly to improving forecasting accuracy and enhancing the company's global asset performance and revenue strategies.
Responsibilities
Knowledge
Impact
Specialisms
General
Experience & Qualifications
Technical Skills
Soft Skills