Weather Quantitative Research Internship Programme

Weather Quantitative Research Internship

Six-Month Internship: 15 February – 12 August 2027

Grenoble (full-time, on site)

Engelhart’s Internship Program offers a unique opportunity to undertake applied quantitative research within a team of experts in Grenoble. The intern will investigate a specific or rare weather phenomenon, explore its relationship with energy-market outcomes, and assess how its forecast could be improved to support trading decisions. To be eligible for consideration, applicants must have the right to work in France and be available to commence their six-month internship from 15 February 2027.

We are also hiring for Weather Analytics, Power Analytics, Renewables Analytics and Renewables Origination Analytics internships in Berlin and Grenoble. These have separate application processes due to their different candidate profiles. Further details are available on our Careers Page.

What You Can Expect

Ownership of a focused quantitative research project with direct relevance to weather forecasting and energy markets.

Exposure to meteorological datasets, numerical weather prediction, AI weather models and advanced forecasting methods.

The opportunity to apply Python and machine learning to a complex real-world research problem.

Close learning and mentorship from experienced weather and quantitative research professionals, alongside exposure to commercial stakeholders.

Key Responsibilities

Study a specific or rare weather phenomenon and develop a rigorous understanding of its underlying physical and statistical characteristics.

Assess the phenomenon’s relationship with energy-market outcomes and its relevance to trading decisions.

Source, clean and analyse complex meteorological and market datasets using Python.

Develop, test and evaluate statistical or machine-learning approaches to improve forecasting accuracy or usability.

Compare relevant numerical weather prediction and AI model outputs, documenting assumptions, limitations and performance.

Present research methods, findings and recommendations clearly to weather, quantitative and commercial stakeholders.

About You

We are looking for an intellectually curious and technically strong researcher who is motivated by the intersection of meteorology, machine learning and energy markets. You should be comfortable working independently on an open-ended analytical problem, testing hypotheses rigorously and communicating complex findings clearly.

The following experiences and skills are essential for application:

Currently undertaking a master’s degree internship or equivalent, including the third year of a French Grande École, in Meteorology, Atmospheric Sciences, Computer Science, Machine Learning or a closely related quantitative discipline.

Strong Python skills, with experience analysing, manipulating and modelling complex datasets.

Strong grounding in machine learning, statistics or related quantitative modelling methods.

Evidence of completing a substantial research, modelling or data-science project through academic study, research, an internship or relevant personal work.

Demonstrable interest in weather forecasting and the application of weather information to energy markets.

Strong analytical and problem-solving skills, including the ability to structure an open-ended research question and evaluate results critically.

Effective written and verbal communication skills, intellectual curiosity and a collaborative mindset.

Business-level English proficiency.

Right to work in France for the full duration of the internship.

Ability to enter into a convention de stage with your university or school for the full duration of the internship.

The following would be advantageous:

Experience working with numerical weather prediction outputs, AI weather models or meteorological data formats.

Familiarity with deep learning, time-series modelling, probabilistic forecasting or model validation.

Experience using weather ecosystem tools or scientific computing libraries.

Exposure to energy markets, power markets, renewables or commodities would be helpful but is not required.