I am a Data Scientist at the UC3M-Santander Big Data Institute (IBiDat) and a PhD student in Statistics for Data Science at Universidad Carlos III de Madrid (UC3M), supervised by Prof. Aurea Grané Chávez. I hold a Bachelor’s degree in Statistics and Business (UC3M, 2023) and a Master’s degree in Big Data Analytics (UC3M, 2024). My work sits at the intersection of statistical methodology, machine learning and applied data science, and I am equally interested in developing new methods and in building the software and production systems that make them usable.

Research interests

• Statistical learning and distance-based methods for complex, large-scale data
• Robust clustering for mixed-type and weighted data
• Applied Statistics
• Generative AI and its applications
• Software development for statistics, machine learning and GenAI (Python packages, APIs, automation)

PhD research

My doctoral thesis, “Statistical Learning Methods Based on Distances and Depths for Large-Scale Data with Complex Structure”, develops statistical learning methods based on distances and depths for large, structurally complex data. My main contributions so far are:

• Robust statistical distances for mixed-type multivariate data (Grané & Scielzo-Ortiz, 2025, SORT), implemented in the Python package robust-mixed-dist.
• New distance-based robust clustering algorithms for large mixed-type data (Grané & Scielzo-Ortiz, 2026, Advances in Data Analysis and Classification), implemented in the Python package db-robust-clust.

These methods have been applied to detecting international opinion groups on the Gaza conflict from Reddit data, combining LLM-based feature extraction with robust mixed-data clustering (with Aurea Grané and Magdalena Díaz-Gorfinkiel, submitted for publication).

Current position

Since September 2024 I have been working as a Data Scientist at IBiDat, where I contribute to applied research and industry projects combining machine learning, time series forecasting and generative AI, including:

• Demand estimation for public parking (Grupo Ruiz) — long-term occupancy forecasting and impact analysis of new commercial and transport infrastructure.
• Interurban public transport demand forecasting (Grupo Ruiz) — long-term bus demand forecasting for the Region of Madrid using time series forecasting and ML-based data generation.
• GenAI virtual assistant (Universia – Banco Santander) — design and deployment (APIs, Docker, MongoDB) of a generative AI system with consultative and evaluator chats to support learning from educational videos.
• Automated video clip generation and evaluation (Universia – Banco Santander) — a tool to generate educational reels/shorts and automatically evaluate their quality.
• Research on the impact of AI in education — a controlled trial measuring how an LLM-based virtual assistant affects learning gains, self-efficacy and cognitive load in video-based learning (submitted for publication).

I also teach: I designed and taught the Python Programming for Data Science module of the ANBAN Data Science Bootcamp (2025), and I taught a session on supervised classification for the Saturdays AI / ANBAN Data Science course at Universidad de Elche (2024).

More about my publications, talks and teaching can be found on the CV, Publications and Talks pages.

Get in touch

Feel free to reach out at fabio.scielzoortiz@gmail.com — I am always happy to discuss statistics, machine learning, or potential collaborations.