A results-driven Data Scientist and Machine Learning professional passionate about building end-to-end predictive pipelines, optimizing statistical models, and translating complex mathematical architectures into actionable corporate insights.
- Graduate with a Bachelor's degree in Economics, fully studied in English at University Carlos III.
- Graduate with a Master's Degree in Data Science, specializing in predictive modeling, advanced classification algorithms, feature engineering, and automated hyperparameter optimization.
- Core expertise lies in designing robust statistical workflows, benchmarking algorithmic performance, handling class imbalance, and maximizing critical operational metrics (such as sensitivity and recall).
- Currently exploring distributed learning infrastructures, production-level MLOps architectures, and advanced cluster-segmentation modeling.
Here is a selection of my core data science, machine learning, and data engineering projects:
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Social Ads Purchase Prediction
Algorithmic classification benchmarking and automated hyperparameter optimization using |
Traffic Accident Analysis in Madrid
Consolidation and deep exploratory data analysis (EDA) of a multi-year municipal corpus encompassing over 312,000 records to isolate statistical distributions and feature correlations for road safety. |
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Global Emissions & Climate Change
Statistical transformation, custom multi-variable reshaping, and data cleaning using advanced functional pivoting techniques to isolate specific environmental pollution metrics by industry. |
Car Depreciation & Market Value
Detailed statistical profiling and data cleansing pipeline on automotive marketplace transactions, implementing domain-specific outlier filtering and mathematical target stabilization. |
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Streaming Platform Content Analysis
Cloud-ready big data engineering pipeline targetting compressed columnar metadata and nested arrays to track international streaming production hubs and release timeline trends. |
Telecom Customer Churn Prediction
Application of advanced statistical inference, logistic regression, and predictive regularization models (Ridge and Lasso) using cross-validation to prevent user attrition and handle corporate dataset balancing. |
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End to End Ticketing BI Pipeline
Design of a complete data warehouse infrastructure using a medallion architecture (bronze, silver, gold) to isolate data extraction and monitor service level agreements (SLA) alongside internal backlog dynamics. |
En camino... 🛠️ Space reserved for upcoming advanced predictive models, clustering frameworks, or neural network architectures. |
 Statistical inference, robust feature engineering, classification benchmarks, and cluster analysis. Expert implementation of cross-validation techniques and hyperparameter optimization to drive real-world KPIs. |
 Pipeline automation and architecture development. Building medallion data infrastructures, distributed data tasks using Spark, and scalable ETL/ELT workflow optimization. |
 Transforming dimensional data models into relational executive summaries. Expert management of advanced data cleaning, relational schemas, and interactive reporting dashboards. |
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