Pablo Olivares Martínez
ML Engineer | Data Scientist
Currently engineering ML at Santander.
§ Summary
ML Engineer and Data Scientist selected for the Santander Data Science Talent Program. Proven ability to apply deep learning and achieve impactful results in data projects, along with a strong foundation in Mathematics and Computer Science.
§ Experience
ML Engineer | Data Scientist @ Santander Bank
Selected for the Santander Data Science Talent Program, now contributing to advanced analytics and scalable data solutions within the Data & AI Models team.
- Developed an LLM explainability solution for predictive models by identifying key features from underlying data to justify commercial opportunities.
- Designed and implemented scalable data pipelines using PySpark for efficient data ingestion for model training.
- Developed a health insurance subscription prediction model achieving 92% AUC, leading to a 23% increase in customer acquisition.
- Designed and implemented a data quality control system for a Customer 360 project, improving data integrity and process execution time by up to 97%.
§ Education
Master of Science in Big Data & Business Analytics @ Escuela de Organización Industrial (EOI), Spain
- Big Data Architecture
- Data Visualization
- Natural Language Processing
Bachelor of Science in Mathematics @ University of Granada, Spain
- Functional Analysis
- Numerical Methods
- Differential Equations
Bachelor of Science in Computer Science @ University of Granada, Spain
- Computer Vision
- Metaheuristics
- Artificial Intelligence
Erasmus+ Programme @ University of Łódź, Poland
- Machine Learning
- Operations Research
- Statistical Inference
- Stochastic Processes
§ Projects
Topological Data Analysis in CNNs
Explored the integration of Topological Data Analysis (TDA) with convolutional neural networks (CNNs) to enhance understanding of CNN data manipulation, resulting in improved classification accuracy and generalization.
- Applied persistent homology techniques to analyze data structure during CNN processing.
- Proposed topological regularization in models like EfficientNet-B0 and DenseNet-121.
- Awarded 'Best Bachelor Thesis 2024 Promotion' for outstanding work.
Semantic Segmentation for Urban Mobility
Developed a deep learning solution for parking space detection in the city of Granada using semantic segmentation techniques, contributing to urban planning and mobility improvement.
- Created an image segmentation dataset with a novel data augmentation technique tailored for parking detection.
- Implemented architectures like PSPNet and DeepLabV3+, achieving an 80% F1-score in validation.
§ Skills
Programming
Machine Learning & Data Science
Technologies & Tools
§ Languages
§ Volunteering
Member and Collaborator @ LibreIM
Developed and maintained extensive LaTeX notes for LibreIM, a non-profit organization, focusing on Formal Logic and Topology.
Private Tutor @ Busca Tu Profesor
Conducted mathematics and basic programming lessons for high school students while managing college studies.
Member @ Imhlala Panzi Scouts
Organized charity and environmental tasks including natural environment cleanups, charity collections to aid minorities, awareness campaigns, and social inclusion activities.