Machine Learning Team Leader2018 - PRESENTMototech
Technologies: Python, R, Sklearn, Tensorflow, Keras
- Developed a forecasting algorithm that predicts the performance of NFL players. The results outperform state of the arte techniques by reducing the MSE from 8.541 to 5.576.
Invited Profesor2018 - PRESENTDigital House
Technologies: Python, Sklearn, Tesnorflow, Bokeh, Matplotlib, Seaborn, Keras
- Dictated theoretical and applied lectures on the following topics: topic detection, conditionality reduction, embedding, time series analysis, embeddings, sentiment analysis, and text analysis/text mining. I used Tensorflow, Python, and keras during the lessons.
Research intern2017 - PRESENTUniversity of Buenos Aires
Technologies: Python, R, Matlab, Sklearn, Tesnorflow, Keras, Xgboost, Catboost, Lightboost
- Analyzed the texts of news using natural language processing techniques. In particular, recursive deep models for semantic compositionality over sentiment treebank in order to detect the sentiment of a sentence, dimensional reduction algorithms and topic detection methods were used with the intention of characterizing the mass media bias during presidential elections.
- Focused my study on developing better machine learning techniques which efficiently uses the information of a node and its neighbours. In addition, This new semi supervised methodology will be validated using graphs of biological and social origin.
Data Scientist2016 - 2017Hexagon Consulting
- Implemented different predictive models in order to describe the future financial behavior of bank clients using Python.
- Designed and implemented a recommendation system that uses text reviews in order to recommend a movie using text analysis (topic detection, sentiment analysis, and word embedding).
- Created and develop a software which statistically calculates a personal index of inflation based on a big economic and financial database.