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Machine Learning and Data Scientist

Pubblicato il 15-08-2026 - ENGIE in Italia

Data Scientist Forecasting 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 B2 B and B2 C customers. It uses its expertise in energy management to provide decarbonized electricity 24/7. Together, we push the boundaries of innovation to deliver decarbonized energy 24/7.



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 experience with energy supply processes, modern data platforms (e.g. Databricks, Spark), and cloud-based analytics environments. As a Forecasting Data Scientist, you will contribute to the development and delivery of forecasting processes and models that support the energy supply business. Under the guidance of the Digital & Portfolio Modelling Manager, you will collaborate closely with forecasting, portfolio managers, pricers, and other business stakeholders to ensure forecasti

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