Senior Data Scientist
I am a Data Scientist with a Ph.D. in Applied Mathematics and Scientific Computing from Campinas University (UNICAMP). I was awarded Best Doctoral Thesis defended in 2017, a distinction granted by The Institute of Mathematics Statistics and Scientific Computing (IMECC). I also hold an MSc. in approximation theory from UNICAMP. I have more than 13 years of combined experience in research, teaching, and work that involves areas such as Artificial Intelligence, Fluid Mechanics, Mining, Metallurgy, and Education. I also have extensive experience as a team leader, creating work strategies and planning teamwork. I am Highly creative and propose unique and specialized solutions to problems.
I have strong coding skills and a solid mathematical background that I apply to escape the usual practices and the resulting stagnation of results. My favorite languages are Python (10 years of experience) and Matlab (more than 20 years of experience). I have extensive experience using Numpy, Pandas, Sklearn, Matplotlib, Seaborn, Tensorflow, Keras, and other Machine Learning Libraries. I have broad practical data modeling experience using all classical machine learning models, e.g., Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), Recurrent Neural Networks (RNN), and Ensemble Algorithms (Boosting, Baggins), to name a few. Likewise, I mastered several feature engineering techniques for missing data handling, feature scaling, categorical variable encoding, feature extraction, creating time series features, creating text data features, and feature selection. Beyond classic tools, my mathematical background allows me to develop models for each model's specificities.
I have led several interdisciplinary projects that include Software Engineers, Biologists, Chemists, Mechanical Engineers, and Graphic Designers, organizing the work to achieve a gear that guarantees the achievement of the project objectives. The projects I have led have their emphasis on Artificial Intelligence with a focus on solving various practical problems. This supposes combining strategies to address each stage, from data exploration to implementing predictive models. I supervised all these stages of the work, and for each, I proposed strategies that combined standard methods and creativity to deal with the particularities of each problem. Every project has enriched my experience building and deploying production-ready machine learning models for use in areas like Mineral prospection, Healthcare, and Education. This has expanded my knowledge of advanced data techniques, including data access, integration, visualization, databases, database design, and data management.
I have extensive experience with Deep Learning techniques such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). I used CNN in a transfer learning process in previous projects to extract image features. Also, I have used Long Short Term Memory (LSTM) RNN in forecasting problems where data consists of time series. For problems including other types of less regular data, like dots in a map, I have used Graph Convolutional Neural Networks to model the problem.
Its policy for a harmonious and productive work design is centered on the individual to balance the abilities and interests of the same to obtain high performance from him and, at the same time, achieve that the work generates satisfaction. Both results go hand in hand, producing a healthy work environment. Likewise, it is very much in his interest to maintain a cordial relationship with the team members and ensure a good interaction that creates synergy between the abilities of each one, inevitably leading to great results. I also adapt quickly and learn continuously.