After completing my Bachelor's in Economics and Mathematics, I pursued a M1 in Data Science for Social Sciences at Toulouse School of Economics (TSE), which provided me with strong foundations in mathematical reasoning, statistics, and econometrics. The program was excellent in developing analytical thinking and quantitative skills, and TSE's rigorous approach really helped me structure my problem-solving methods. However, I found that the M1 was more theory-focused and offered relatively few applied, hands-on projects, which made me realize the importance of complementing theoretical knowledge with practical experience.
At the end of my M1, I completed a 4-month internship at Avisia as an AI Consultant, which was extremely enriching. During this internship, I had the opportunity to work on a real-world project where I developed practical skills in AI and data science. I learned how to design and implement technical solutions to automate processes, and integrating AI tools into business workflows. I also gained experience in using Python, Docker, and Streamlit, which allowed me to see how solutions move from the development phase to deployment. Beyond technical skills, this experience taught me how to communicate effectively with team members and stakeholders, understand their needs, and translate complex technical concepts into actionable insights. It was a formative experience that confirmed my passion for applied data science and the value of combining analytical skills with problem-solving in real business contexts.
For this last year, I moved to Paris for my M2 in Data Science at Sorbonne University, where I am currently doing a work-study as a Data Scientist at Engie Enterprises and Collectivities. This position allows me to apply the knowledge and skills I have acquired throughout my studies in real-world business contexts. I am actively working on developing predictive models and, at the same time, discovering the energy sector and its specific challenges.
Overall, my academic path, from the L3 in Economics and Mathematics, through the M1 at TSE, and my professional experiences at Avisia and Engie, has shown me the importance of combining solid theoretical foundations with practical, project-based learning. TSE provided me with excellent analytical training, while my internships and work-study experiences allowed me to gain hands-on expertise and confidence in applying data science and AI to real-world problems. My advice to TSE students would be to never give up, remain curious, and always seek to learn new things, because continuous learning and persistence are key to succeeding in the fast-evolving fields of data science and AI.