I'm a Data Science student based in Valencia, Spain, interested in using data to solve problems that have a clear practical outcome.

Most of my work sits somewhere between machine learning, analytics, optimization and software, often involving large datasets, forecasting, model evaluation or decision-support systems.

I particularly enjoy projects where the technical side is only part of the challenge — understanding the problem, working with constraints and turning the result into something useful matters just as much.

I'm currently especially interested in applications of data across operations, technology, fintech, business analytics and quantitative decision-making.

Forecasting, optimization and decision support for urban mobility

Python · CatBoost · FastAPI · PuLP · Docker · GitHub Actions · TypeScript

• Built as an end-to-end platform combining traffic forecasting with infrastructure allocation.
• Analysed more than 850,000 observations across 1,158 geographic zones.
• Developed 24 predictive models, reaching R² = 0.92 on unseen temporal data.
• Added an Integer Linear Programming model to allocate resources under budget constraints.
• Extended beyond modelling with APIs, deployment, monitoring and scenario analysis.
• Academic project awarded 10/10.

Large-scale NLP analysis of scientific research

Python · NLP · Transformers · LLMs · Docker

• Analysed more than 31,000 scientific abstracts.
• Compared lexical baselines, scientific embeddings and locally hosted LLM approaches.
• Designed an auditable classification pipeline around the Planetary Boundaries framework.
• Focused on model comparison, false-positive control and uncertainty.
• Academic project awarded 9.9/10.

Hierarchical multimodal classification

Python · NLP · Computer Vision · Transformers · Ensembles

• Co-developed a multimodal system combining textual, visual and physiological information.
• Worked with multilingual embeddings, computer-vision representations and supervised ensembles.
• Performed model comparison, calibration, ablation experiments and error analysis.
• Reached up to 0.709 macro-F1 in internal validation.
• Ranked Top-5 in the LNR academic challenge based on EXIST 2026.

Global socioeconomic and health-data exploration

R · Shiny · Plotly · APIs

• Integrated economic and health indicators covering around 190 countries.
• Worked with GDP per capita, healthcare expenditure, population and mortality data.
• Developed an interactive analytical application for international comparison and visualization.
• Combined statistical analysis with dashboards, rankings and geographic exploration.

At the moment, I'm especially interested in working on problems related to:

• Applied Machine Learning
• Business & Data Analytics
• Operations Research
• Forecasting and optimization
• Fintech and quantitative finance
• Data-driven product and operational decisions
• AI applied to real business problems

I'm also building new projects around financial analytics, fintech and decision-support tools to complement my current technical portfolio.