Lionel Voirol

Statistician and Data Scientist with a Ph.D. in Statistics and a Master's degree in Business Analytics. I combine statistical modeling, machine learning, and software engineering to solve business and scientific problems using data.

During my Ph.D., I developed statistical methods driven by real-world applications across engineering, biomedical sciences, and Earth sciences. In particular, I developed scalable statistical methods for inference, model selection, and prediction in large-scale datasets with temporal and spatial dependence, with applications to inertial sensor calibration and Earth Sciences. I implemented these methods as reproducible open-source tools, including production-ready R packages distributed on CRAN, supporting large-scale data processing and production-level analytical workflows.

I have collaborated with interdisciplinary teams across academia and industry, including the University of Geneva, Geneva University Hospitals (HUG), Pharma24, Hexagon, EPFL, Brookhaven National Laboratory, and Auburn University, translating scientific and business challenges into practical quantitative solutions.

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