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Associate - Data Science @Itaú Asset Management
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WORK HISTORY
Associate - Data Science @Itaú Asset Management
EDUCATION
UC Santa Barbara
Supervised Research - Summer Internship, Stochastics Calculus for Finance - Binomial Model
USP - Universidade de São Paulo
Master's degree, Statistics
Tompkins Cortland Community College
Intensive English Program
ETEC Presidente Vargas
Hardware Architecture Technician -, Computer Engineering
Liceu de Artes e Ofícios
Technical in Industrial Mechanics -, Mechanical Engineering
Stony Brook University
Bachelor's degree, Statistics
USP - Universidade de São Paulo
Bachelor's degree, Statistics
ETEC Presidente Vargas
Mechatronics Technician - , Robotics and Automation Engineering
ABOUT WILSON DOS ANJOS
I am a Statistician and Data Scientist. As a Data Scientist, I’ve participated in projects in which I developed end-to-end solutions from- Designing architectures and developing MVPs prototypes of solutions I presented to clients- Working on the gathering of multiple sources datasets from sensors, to APIs and data from webscraping, which would be worked upon later on the pipeline- Setting up EMR instances on Cloud services, such as AWS, on which the model would be deployed- Working on the whole ETL, generating features from the raw data and selecting them with previous (internally) developed techniques that would help to improve accuracy of ML models- Modeling using numerous types of ML models, from xgboost trees, to Deep Learning models such as convolutional nets, recurrent nets, LSTMs, Attention modules, to Ensemble Methods (on Rstudio, and Keras and Tensorflow in Python)- Using of interpretation methods, such as LIME and SHAP, to check upon any spurious correlation and/or help on clients’ decision making- Deployment of models on the Cloud and on-premise- Creation and updating of logs to keep track of the well functioning of the pipeline- Creation of multiple Dashboards on Shiny and Tableau to show the results of the models in realtime- To finally, doing the homologation and due maintenance of the ML models at the end of projects.All of those in multiple different sectors such as- Fraud Prevention- Energy Efficiency- Churn- Recommendation systems- Stocks Portfolio Optimization- Preventing failures on Industrial Machines- Logistics- Image Recognition- Demand forecast- Credit Scoring- Supply Chain.
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