Machine Learning, Data Science, and Software Development - Intern
- Suisse
- Stage
- Temps-plein
Reinforcing the competencies of our people is a key pillar of our culture: we train our people to ensure their development and we pledge to encourage safety in all our actions.We look for passion, ambition and open-mindedness. While we evolve in a demanding industry that requests to be always on the edge, we cultivate a friendly workplace where our people feel good, where team spirit and respect guide our daily routine, where the diversity of our people and their skills create a nourishing experience for all of us.The Department of Modelling and Analysis for Trading Strategies at TotalEnergies Gas and Power leverages machine learning and algorithmic trading to deliver actionable insights to trading desks. Based in Geneva, the team operates in a dynamic environment influenced by evolving market trends, climate policies, and advancements in the energy sector. Focused on creating practical and lasting solutions, the team develops robust software products such as advanced Auto-ML libraries. With expertise in machine learning, data science, and software engineering, the team is committed to delivering impactful tools that drive innovation and efficiency.This internship offers a unique opportunity to gain hands-on experience in a real-world setting, contributing to the company's goal of achieving Carbon Neutrality by 2050.ActivitiesActivities:
- Assist in the development of advanced AutoML time-series forecasting libraries tailored for trading, energy management, and optimization. Contribute to innovation in data science and machine learning through both applied development and research, exploring state-of-the-art methods to enhance forecasting accuracy and model robustness. Participate in software engineering challenges, code reviews, testing, and integration processes to ensure high-quality, production-ready solutions
- Double Machine Learning for causal inference in energy markets - Conduct research on applying DML methods to estimate causal effects, improving decision-making and trading strategies through robust causal analysis.
- Extension of AutoML capabilities for time-series forecasting - Develop and test new modules to expand existing ML libraries, improving scalability, robustness, and user experience for applications in energy trading, management, and optimization.
- Evaluation metrics research for forecasting models - Investigate and define optimal KPIs for assessing time-series forecasting performance, focusing on practical relevance, interpretability, and measurable business impact.
- Currently pursuing a Master's degree in Computer Science, Data Science, Mathematics, Physics, or a related field.
- Strong interest in machine learning, data science, and software engineering.
- A solid understanding of data science/machine learning principles and familiarity with software development, or strong software development skills with a working knowledge of data science and machine learning concepts.
- Proficiency in Python and familiarity with ML libraries such as Scikit-learn, XGboost, TensorFlow, etc.
- Eagerness to learn and innovate, with a proactive approach to problem-solving.