Francesco Pinto

Passionate about AI, Deep Learning, and Data Science, I specialize in developing cutting-edge machine learning models for real-world applications. My expertise spans Computer Vision, Probabilistic Modeling, and Large Language Models, with hands-on experience in research and industry.

With a Master’s degree in Data Science and a Bachelor's degree in Statistics from La Sapienza – University of Rome and an Exchange semester at the University of Helsinki, my academic background is rooted in statistics, probabilistic modeling, and AI.

Beyond my professional work, I am passionate about bridging AI research and industry applications, constantly seeking innovative ways to leverage machine learning for impactful solutions.

Deep Learning Scientist Intern – MeteoSwiss (2024–2025)
Developed deep learning models (CNP, U-Net) in PyTorch to improve quality control of precipitation data by integrating ground and radar measurements. Conducted literature review, model evaluation, and internal presentations.

MSc Thesis – Eawag (2023–2024)
Researched how normalization layers (BatchNorm, LayerNorm) affect bias in untrained neural networks ("Initial Guessing Bias"). Combined theoretical analysis with practical experiments; results prepared for publication.

Junior Data Scientist – Hyntelo (2023)
Worked in R&D and consulting. Built ML, LLM, and Computer Vision models; developed dashboards (Tableau, QlikSense); designed web scraping tools and supported data-driven strategies for clients in finance and pharma.

Master's degree in Data Science focused on Machine Learning, Inferential Statistics, Data Mining, Bayesian Inference, Data Management, Networking for Big Data, Data Driven Economics, Deep Learning.

Graduated with 110/110 cum Laude (Honors).