Lionel Voirol

Statistician and Data Scientist with a Ph.D. in Statistics and a Master's degree in Business Analytics. I combine advanced statistical modeling with software engineering to solve complex business and scientific problems using data. I develop statistical methods and machine learning models that transform real-world data into reliable insights for decision-making.

During my Ph.D., I developed statistical methods driven by real-world applications across engineering, health sciences, and Earth sciences. In particular, I developed new computationally efficient statistical methodologies 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 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.

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