Hybrid Data Assistant – Power & Renewables Analytics in Edinburgh

Hybrid Data Assistant – Power & Renewables Analytics in Edinburgh

Edinburgh Full-Time 50000 - 60000 Β£ / year (est.) No working from home possible
Wood Mackenzie Ltd

At a Glance

  • Tasks: Support the global power industry by collecting and analysing data.
  • Company: Join Wood Mackenzie, a leader in Power and Renewables analytics.
  • Benefits: Enjoy a hybrid work model, competitive salary, and growth opportunities.
  • Other info: Collaborate with a diverse team across time zones in a dynamic environment.
  • Why this job: Make a difference in the renewable energy sector while developing your skills.
  • Qualifications: Strong analytical skills and attention to detail are essential.

The predicted salary is between 50000 - 60000 Β£ per year.

Wood Mackenzie Ltd is looking for a Data Assistant to join their Power and Renewables Project Tracking Data team in Edinburgh. This hybrid role focuses on data collection through research to support the global power industry. The ideal candidate will possess strong analytical capabilities, problem-solving skills, and a keen attention to detail.

Responsibilities include:

  • Monitoring power plant data
  • Collaborating across time zones
  • Improving data quality

Hybrid Data Assistant – Power & Renewables Analytics in Edinburgh employer: Wood Mackenzie Ltd

Wood Mackenzie Ltd is an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation within the Power and Renewables Analytics team in Edinburgh. Employees benefit from flexible hybrid working arrangements, comprehensive professional development opportunities, and a commitment to sustainability, making it a rewarding place for those passionate about driving change in the global power industry.

Wood Mackenzie Ltd

Contact Details:

Wood Mackenzie Ltd Recruitment Team

We think you need these skills to ace Hybrid Data Assistant – Power & Renewables Analytics in Edinburgh

Analytical Capabilities
Problem-Solving Skills
Attention to Detail
Data Collection
Research Skills
Data Quality Improvement
Collaboration Across Time Zones