Senior Data Scientist @ IE University

June 2025 - Present

I work on climate risk data science at IE Insurance and Tech Lab, drawing on methods from ML, statistics, and climate science to address real insurance problems.

• Built Discrete Lag Non-linear Models (DLNMs) and Relative Risk (RR) curves to support climate risk analysis for insurers.
• Built interactive visual dashboards (Flask, PythonAnywhere) to communicate climate risk results to insurers and the public.
• Implemented Kolmogorov-Arnold Networks (KANs) for weather forecasting, applying a new architecture to a practical domain problem.
• Presented research findings in Steering Committee sessions with Vienna Insurance Group, in front of industry leadership and academic partners.

Highlight: Launching the IE Insurance and Tech Lab.

COO @ Canonical Green

Apr 2023 - Present

Canonical Green develops data science solutions for the ecological transition of the maritime industry.

• Built and deployed route optimization models for maritime clients, targeting reductions in fuel use and carbon emissions of up to 10%.
• Presented to diverse audiences including investors, funding juries, institutional stakeholders, and the general public, adapting the message for each setting.
• Showcased the project at Prototypes for Humanity 2024 in Dubai, selected among 100 innovations from over 2,000 global submissions.
• Invited onto The Conversation Weekly podcast to discuss maritime decarbonization for a broad audience.
• Awarded a Torres Quevedo Grant (2023) by the Spanish Ministry of Science (grant declined for strategic reasons).
• Helped secure €93K in competitive funding from Missions València 2030, with our project ranked top by the jury.

Highlight: Presented the technology at the European Parliament.

Data Scientist @ Komorebi AI

Sep 2022 - Apr 2023

I worked as a full-stack data scientist on applied projects for industrial clients, quickly adapting to a new problem domain (marine biology, computer vision) and building end-to-end solutions within months.

• Cleaned and processed 500K+ data points from raw sensor data, enabling accurate fish population monitoring for overfishing prevention.
• Designed and deployed gradient-boosted and CNN models using scikit-learn that achieved 90%+ accuracy in real-time tuna detection.
• Built an interactive Streamlit dashboard used daily by the team for operational decisions, reducing analysis time by 80%.

Predoctoral Research Staff @ University of Cádiz

Sep 2019 - Aug 2022

I contributed to multiple research projects with industrial partners, building machine learning solutions for applied problems.

• Developed machine learning models for Industry 4.0 applications in energy monitoring and sustainable fishing, in collaboration with industrial partners.
• Collaborated with Spain's Health Alerts Centre to develop COVID-19 ICU forecasting models used to support national policy decisions during the pandemic.
• Presented research at international conferences to technical and mixed audiences.
• Published 5 peer-reviewed papers in Q1/Q2 journals on applied machine learning and data science.

Junior Data Scientist @ Foqum

Jan 2019 - Jun 2019

My first industry role, working on real ML problems in the energy sector alongside experienced engineers.

• Built and tested neural network models using Keras and TensorFlow for non-intrusive load monitoring (NILM) to estimate appliance usage from aggregate power data.
• Contributed to energy disaggregation projects that helped optimize power consumption analysis.
• Picked up version control, reproducibility, and professional workflows for presenting technical results in a team setting.

Postdoctoral Researcher & Adjunct Professor @ IE University

Sep 2023 - Present

I work at the intersection of applied research and teaching, and genuinely enjoy both sides of the job.

• Built climate impact models simulating insurance risk under SSP-RCP scenarios, used by VIG to inform portfolio analysis.
• Presented research at the ICMS Climate Change and Insurance 2025 workshop in Edinburgh, to an audience of actuaries, climate scientists, and industry representatives.
• Implemented Kolmogorov-Arnold Networks (KANs) for weather forecasting, applying a new architecture to a practical domain problem.
• Developed optimization algorithms for weather-based ship routing aimed at reducing GHG emissions.
• Built and maintained IE Research Datalab's official website to improve the research group's online presence.
• Delivered 4 courses (Computer Programming I, Coding Lab, Time Series Analysis, Applied Math Lab) to 100+ students; received a Teaching Excellence Award from students in 2025.

PhD in Computer Engineering @ University of Cádiz

Sep 2019 - Jul 2023

Thesis: "Applications of Machine Learning and Data Science to the Blue Economy" under the supervision of David Gómez-Ullate.

• Collaborated with industrial clients in healthcare, energy, and marine transport to build applied ML solutions.
• Developed machine learning models for Industry 4.0 applications in energy monitoring and sustainable fishing, in collaboration with industrial partners.
• Collaborated with Spain's Health Alerts Centre to develop COVID-19 ICU forecasting models used to support national policy decisions during the pandemic.
• Presented research at international conferences to technical and mixed audiences.
• Published 5 peer-reviewed papers in Q1/Q2 journals on applied machine learning and data science.

Highlights: Cum Laude distinction -- International awards in sustainability and computer vision -- COVID response research

MSc in Statistical and Computational Information Processing @ UPM

Sep 2018 - Jul 2019

I completed my master's degree specializing in data science, acquiring essential skills in data analysis, data mining, neural networks, and Big Data technologies.

Degree in Physics @ Complutense University of Madrid

Sep 2014 - Jul 2018

I completed my bachelor's degree in Physics, earning honors in four subjects related to Quantum Physics and Astrophysics. This foundation in rigorous analytical thinking prepared me for advanced work in data science and machine learning.