Machine Learning Engineer
* Automatic product categorization for e-commerce: I designed a system based on Natural Language Processing (NLP) to automatically assign categories to products in marketplaces and super apps. This streamlines inventory loading for sellers, optimizes user experience and facilitates more accurate searches. Improved navigation and efficient taxonomy increase conversions and ensure a smooth shopping experience. * Sentiment analysis in reviews for streaming services: I created a model to classify sentiment in movie reviews using techniques such as Bag of Words (BoW) and TF-IDF for text vectorization. In addition, I trained a custom word embedding and a model to identify positive and negative opinions, helping platforms to better interpret feedback, personalize recommendations and optimize marketing strategies.* Sentiment analysis in reviews for streaming services: I created a model to classify sentiment in movie reviews using techniques such as Bag of Words (BoW) and TF-IDF for text vectorization. In addition, I trained a custom word embedding and a model to identify positive and negative opinions, helping platforms to better interpret feedback, personalize recommendations and optimize marketing strategies.* Image classification for e-commerce: I implemented a Convolutional Neural Network (CNN) to identify the make and model of vehicles through unstructured images, using a dataset of 196 classes. By applying data augmentation and after cleaning the data, I achieved an accuracy of 82%. The solution was deployed on AWS using Docker and APIs to integrate with web platforms.* Mortgage credit risk analysis: I developed a predictive model to assess the risk of default on mortgage loans based on more than 350,000 transactions. I used advanced preprocessing and trained models such as Decision Trees, XGBoost and LightGBM, achieving a ROC AUC above 0.72. This tool improves credit decisioning and optimizes portfolio management through more accurate evaluations.
