Lionel Voirol

Statistician and Data Scientist completing a Ph.D. in Statistics at the University of Geneva under the supervision of Prof. Stéphane Guerrier, with a Master’s degree in Business Analytics. I combine advanced statistical modeling, machine learning, and software engineering to build scalable solutions for complex data-driven problems.

My work focuses on developing computationally efficient methods for inference, prediction, and model selection in large-scale datasets with temporal and spatial dependence. I translate these methodologies into robust open-source software, developing high-performance analytical tools and R packages distributed on CRAN that enable reproducible, scalable, and efficient data analysis.

I have collaborated with interdisciplinary teams across engineering, Earth sciences, biomedical sciences, and business analytics at the University of Geneva, the École Polytechnique Fédérale de Lausanne (EPFL), the Brookhaven National Laboratory, and Auburn University, translating scientific and business challenges into practical quantitative solutions that support data-driven decision-making under uncertainty.

I am currently seeking quantitative roles in data science, quantitative research, quantitative analytics, or machine learning within organizations that value rigorous methodology, real-world impact, and well-engineered data systems.