Data Scientist Forecasting
Pubblicato il 10-08-2026 - ENGIE Italia in Milano
WHO IS ENGIE?
ENGIE puts sustainable development at the center of its activities (electricity, natural gas, energy efficiency, and services) to meet the challenges of the energy transition to a low-carbon economy: access to a sustainable economy, climate change mitigation and adaptation, and resource optimization.
ENGIE, a global leader in low-carbon energy and services, relies on its Global Business Unit Supply & Energy Management (GBU S&EM;) to provide reliable, sustainable, and affordable energy to all its customers. This strategic unit optimizes the Group’s and clients’ production assets and designs tailored energy solutions for our 200,000 professional clients and 15 million consumers.
The Global Business Unit Supply & Energy Management leverages ENGIE’s assets to deliver secure and sustainable energy to its B2B and B2C customers. It uses its expertise in energy management to provide decarbonized electricity 24/7.
Joining Supply & Energy Management means becoming part of a team of over 10,000 passionate experts spread across 20 countries, all united by a shared mission:
shaping a greener and more efficient energy future. Together, we push the boundaries of innovation to deliver decarbonized energy 24/7. Join us and be part of those shaping the energy of tomorrow
WHO ARE WE LOOKING FOR?
We are looking for a Forecasting Data Scientist in Supply & Energy Management Italy, based in Milan.
The ideal candidate is a data-driven professional with a strong quantitative background and hands-on experience in data science and machine learning, particularly in time series forecasting. The role combines solid programming skills (Python and R) with a practical, business-oriented mindset and the ability to transform complex data into actionable insights.
He/she has curiosity for energy markets and the energy transition, along with the willingness to deepen knowledge of market design and regulatory frameworks. He/she has experience with energy supply processes, modern data p