Saas in Climate Tech
Optimizing energy flexibility to accelerate the transition ⚡️
A climate startup that uses tech and data to manage the electricity consumption of buildings in real time — by promoting their energy flexibility and by contributing to the balance of the network. A €5 million fundraiser has just been finalized to change scale.
Description
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.
Mission
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.
Profil recherché
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.
Déroulement des entretiens
- Initial exchange (30 min) — To get to know each other, validate the overall adequacy.
- Technical maintenance (2 hours) — Practical case prepared in advance (1 hour) + technical exchange (1 hour).
- “Fit” interview (2h) — Discussions with the team on motivation, soft skills, vision, and the collective project.
