¡Hola! I'm Pablo Matorras-Cuevas, a data scientist and PhD physicist specialising in Machine Learning and Large Language Models (LLMs). With over six years of experience in statistical modelling and high-dimensional data analysis, I combine scientific rigour with modern AI techniques to solve real-world problems.
"While my academic base was in my hometown, I actively sought a global perspective, completing exchange programs in the UK, USA, and Switzerland."
Machine learning system for equity selection that predicts S&P 500 outperformers using market data, fundamentals, and sentiment indicators. Achieved 20.2% annual returns and 0.93 Sharpe ratio using time series cross-validation and Random Forest models.
Advanced NLP pipeline fine-tuning FinBERT for financial sentiment analysis. Implements a multi-task architecture (classification + regression) to handle diverse data from news, social media, and forums, achieving 85.4% accuracy.
Interactive dashboard for exploring macroeconomic indicators (inflation, GDP, etc.) across countries. Built with Plotly/Dash and Python, featuring time series analysis and geospatial visualization.
End-to-end system predicting match outcomes (home/draw/away) across Europe's top five leagues. Combines Elo ratings with machine learning (RandomForest) achieving 50.5% accuracy on three-class prediction and a 37.4% draw recall. Includes an optional Plotly/Dash visualization.
2019 - 2025 | Santander, Spain & Geneva, Switzerland
2021 - 2023 | Santander, Spain
Jul 2018 - Jul 2019 | Zurich, Switzerland
Trained in statistical modeling and large-scale data analysis through research at CERN and academic programs across Europe and North America.
2019 - 2025 | Santander, Spain / Geneva, Switzerland
PhD Thesis:
"Search For Supersymmetric Particle Pair Production In Final States With Two Oppositely Charged Leptons And Large Missing Transverse Momentum In Proton-proton Collisions At 13 TeV"
Supervisor: Luca Scodellaro | Cum laude
2017 - 2018 | Santander, Spain / Zurich, Switzerland
Master Thesis:
"Higgs production cross section at 13 TeV and prospects on BSM searches for the HL-LHC"
2013 - 2017 | Santander, Spain / London, UK / Pittsburgh, USA
During my Bachelors and Masters, I also participated in several exchange programs that enhanced my international perspective, including:
| University College London, UK | (Sep 2015 - Jun 2016) | |
| University of Zurich, Switzerland | (Jul 2018 - Jul 2019) | |
| University of Pittsburgh, USA | (Aug 2015 - May 2016) |
CERN Summer Student | Geneva, Switzerland | Jun – Sep 2017
Participated in the CERN Summer Student Programme, which combined specialised lectures from leading scientists with hands-on project work. Worked with the RD50 R&D group developing automated analysis for silicon sensor testing results ( Capacity-Voltage and Current-Voltage measurements).
Participated in specialised summer schools advancing expertise in particle physics, machine learning, and quantum computing:
Presented research at 8+ international conferences, including ICHEP2022, SUSY2023, and DIS2024. Topics span supersymmetry searches, electroweak SUSY production, and detector performance with the CMS collaboration. Represented CMS at major venues across Europe and North America.
h-index: 59 | 12,520 citations across 356 citable papers with average 39 citations per paper. Contributions include PhD thesis on supersymmetric particle searches, CMS Public Analysis Summary (SUS-23-002), and peer-reviewed conference proceedings. Full profiles available on InspireHEP and Google Scholar.