Join a dynamic Data Science team and work on complex issues at the intersection of energy and data.
You will handle massive and varied data sets (time series, sensors, business data...), in a sector in full structuring, where everything remains to be invented.

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As a Machine Learning Engineer, you will be involved in all stages of ML model development, with a particular focus on energy-related issues.

You will contribute to:

• Design end-to-end machine learning solutions: problem definition, development, evaluation.
• Apply the latest best practices in the field and stay on the lookout for research progress.
• Deploy and monitor models in production: versioning, supervision, continuous improvement.
• Collect, clean, and analyze large volumes of heterogeneous data.
• Collaborate with internal teams to clearly identify business needs and contribute to building a coherent and robust data platform.

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Indispensable:

• Minimum 3 years of experience (or doctorate) in applied mathematics, computer science or a related field.
• Solid understanding of the theoretical foundations behind the algorithms used.
• Proficiency in one or more programming languages (with a strong preference for Python).
• Demonstrated experience on time series forecasting projects.
• Autonomy, intellectual curiosity and the ability to propose new approaches.

Additional benefits:

• Control of one or more cloud environments.
• Strong interest in energy and climate issues.

High-potential profiles are encouraged to apply, even without ticking all the boxes. The team values the desire to learn, adaptability and the ability to evolve with the position.

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1. Initial exchange (30 min) — To get to know each other, validate the overall adequacy.
2. Technical maintenance (2 hours) — Practical case prepared in advance (1 hour) + technical exchange (1 hour).
3. “Fit” interview (2h) — Discussions with the team on motivation, soft skills, vision, and the collective project.

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