Data Analyst • Applied Statistician • Business Intelligence • Machine Learning
Transforming complex data into reliable insights through statistics, analytics and AI.
I'm a Data Analyst with a strong background in Applied Statistics, Business Intelligence and Machine Learning.
I hold two Master's degrees in Applied Statistics and Business Intelligence & Big Data, combining statistical theory with practical experience in analytics, predictive modelling and business intelligence.
My experience as a University Lecturer has strengthened my analytical thinking and my ability to communicate complex technical concepts to diverse audiences.
Currently based in Valencia, Spain, I am looking for opportunities where I can apply statistics, programming and machine learning to solve real-world business problems.
- Building end-to-end Data Analytics projects
- Machine Learning for Healthcare
- Business Intelligence with Power BI
- Deep Learning for Drug Discovery
- Open Source Portfolio Development
- Pandas • NumPy • Scikit-learn • PyTorch • XGBoost
- Power BI • Tableau • SQLite
- Git • GitHub • VS Code • Jupyter Notebook
Tech: Logistic Regression • Random Forest • XGBoost
Developed predictive models to estimate customer default risk and support data-driven lending decisions through threshold optimisation and model evaluation.
Tech: Python • SQL • Power BI
Built an end-to-end analytics pipeline from data preparation and SQL modelling to interactive dashboards for customer and sales analysis.
Tech: PyTorch • RDKit • ResNet50 • PubChem
Designed a deep learning pipeline that classifies therapeutic drug classes from multi-view 3D molecular representations using convolutional neural networks.
I enjoy working at the intersection of:
- Applied Statistics
- Machine Learning
- Computer Vision
- Healthcare Analytics
- Drug Discovery
- Predictive Modelling
- Explainable AI
📍 Valencia, Spain
🐙 GitHub
---"Without data, you're just another person with an opinion." — W. Edwards Deming