Marlon.
Computer Science Engineer
Data Scientist
Passionate about the development of artificial intelligence models for discovering the stories behind data.
Hi, my name is
About Me
I am a data scientist pursuing a Master’s degree in Data Science at the Autonomous University of Madrid, currently working on my Master’s thesis with a focus on artificial intelligence. I hold an engineering degree in Computer Science from San Francisco de Quito University. Throughout my career, I have developed and implemented numerous machine learning models that drive business value. My professional experience includes working at Banco Solidario S.A., where I led data analytics projects and developed interactive dashboards to enhance customer experience and sales efficiency.
I have expertise in various artificial intelligence techniques and statistical analysis, I have worked with machine learning and deep learning for Natural Language Processing (NLP), Computer Vision, Biometrics and recommendation systems. I have earned several certifications from MITx MicroMasters in data analysis, statistics, and machine learning. My research, published in IEEE, highlights my contributions to machine learning and path planning optimization. Additionally, I am fluent in both Spanish and English.
Here are a few technologies I've been working with recently:- Python
- R
- Java
- SQL
- Spark
- C++
- Dart
- C#
- CSS
- HTML
- LaTeX
Education
Master's Degree in Data Science
Universidad Autónoma de Madrid (UAM)
Sep 2023 - Present
GPA:
7.73 out of 10
I expect to graduate in January 2025.
Extracurricular Activities:
- Delegate of the Master’s Degree in Data Science.
- Advanced Methods in Machine Learning.
- Advanced Methods in Statistics.
- Deep Learning for Biometric Information Processing.
- Deep Learning for Signal Processing Image and Video.
- Large-Scale Data Processing.
- Natural Language Processing (NLP).
- Reinforcement Learning.
- Temporal Information Processing.
- Unstructured Information.
- Data Management.
Degree in Computer Science Engineering
Universidad San Francisco de Quito (USFQ)
Aug 2017 - Jun 2021
GPA:
2.94 out of 4
I published a paper in the 2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM).
Some relevant subjects taken:- Artificial Intelligence.
- Calculus (I, II and III).
- Data Structures and Algorithms.
- Data Mining.
- Databases.
- Linear Algebra.
- Programming (I, II and III).
- Probability and Statistics.
- Systems Design.
- Projects: Management and Analysis.
Experience
Data Analytics Officer
Banco Solidario S.A.
Jan 2022 - Ago 2023
- Increased the balance in savings accounts by USD 200,000 by identifying over 38,000 potential clients who increased their balance by more than USD 260 through the implementation of an XGBoost model, with 7% of them achieving this increase.
- Lead and work closely with product owners to develop successful projects, communicating findings and results clearly and effectively to non-technical audiences.
Data Analytics Technician
Banco Solidario S.A.
Jul 2021 - Dic 2021
- Increased the number of downloads of Banco Solidario’s mobile app by 30% and reduce the cost per download by 22% by implementing a Random Forest model to identify potential customers for digitalization, also improving customer segmentation.
- Enhanced customer experience and boost sales by developing an interactive dashboard to monitor the efficiency of time in sales and services for commercial advisors at Banco Solidario, enabling targeted actions at each branch.
Publications
Path Planning Optimization in SDN Using Machine Learning Techniques
2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM)
Oct 2021
Certificates
Achievements
Ideatón 2023 of BANCO SOLIDARIO S.A.
We achieved second place for the solution proposed to a strategic challenge of the bank.
Some Projects
Python
Scikit-learn
Machine Learning (ML)
Gradient Boosting Implementation
This project features an implementation of the Gradient Boosting algorithm, an ensemble method that combines multiple decision trees (stumps). It utilizes gradient descent optimization to minimize the loss function. The collective contributions of all weak models (stumps) result in a robust predictive model.
Python
WordNet
Beautiful Soup
Natural Language Processing (NLP)
Analysis of Emotions in Classic Novels
This project uses Natural Language Processing (NLP) to analyze emotions in literary texts from Project Gutenberg, aiming to identify and quantify emotions through advanced NLP methods like sentiment analysis and text information extraction.
Python
Reinforcement Learning (RL)
Q Learning and SARSA Implementation
This project demonstrates the implementation of two reinforcement learning algorithms: Q Learning and SARSA. These algorithms are evaluated across various grid world maps to analyze their performance and behavior.
Python
PyTorch
Computer Vision
Multiple Object Tracking for Video Sequences
This project addresses the task of multiple object tracking (MOT), specifically focusing on tracking people walking in video sequences. The base model for detection and tracking is enhanced using advanced techniques to improve performance. The dataset used is MOT16, which contains various scenarios for individual detection.
Python
TensorFlow
Keras
Recommendation Systems
Recommendation: Matrix Factorization and Deep-Learning
This project demonstrates collaborative filtering on the movie ratings dataset using matrix factorization combined with neural networks.
Get in Touch
My inbox is always open. Whether you have a question or just want to say hi, I’ll try my best to get back to you!