Data Scientist Forecasting

Location: Turbigo

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 platforms (e.g. Databricks, Spark), and cloud-based analytics environments. Strong communication skills and the ability to clearly explain technical concepts to non-experts complete the profile.

WHAT WILL YOU DO?
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 forecasting models are accurate, timely, and aligned with operational needs. Your work will be based on rigorous data collection and analysis, and will directly contribute to optimizing costing, forecasting, sourcing, and hedging strategies within a defined business mandate.

You will be actively involved in the design, implementation, and continuous improvement of forecasting tools and methodologies, ensuring alignment with global standards and contributing to their evolution.

Your role will also include cross-functional collaboration within the SPM community and with data and forecasting teams in other countries to promote knowledge sharing and consistency.

You will be responsible not only for the technical development of forecasting models but also for ensuring their operational reliability and relevance to business needs. You will provide scientific expertise for bespoke analyses and contribute to the continuous improvement of forecasting practices.

Key Responsibilities

Model and Process Development:  Contribute to the design, implementation, and continuous improvement of forecasting tools, models, and processes used to support the energy supply business.

Commercial Offer Support:  Participate in the development of forecasting components for new commercial offers, ensuring alignment with business needs and market dynamics.

Customer Insights:  Analyze customer behavior using diverse data sources to enhance forecasting accuracy and business relevance.

Data Utilization:  Leverage a wide range of internal and external data sources to ensure robust and accurate portfolio and consumption representation.

Best…