I have experience in different segments of industry, such as strategic
consulting, financial, telecommunications and technology. All these
experiences helped me to improve my statistical and analytical
Data Scientist
Sao Paulo, BR
[email protected]
Development and maintenance of machine learning models to predict LTV and support the user acquisition
Tools: Python, R, Spark
I worked on the Deep Analytics team identifying and acting on opportunities to apply machine learning on different areas of the company to increase revenue and make processes more efficient.
I worked on the Machine Learning Team developing statistical models and data analysis to support decisions of several internal clients on Movile
I used tools like R, Python, Microsoft Excel and SQL to extract as much value as possible from data and improve results.
A few examples of Data Science projects that I worked are : Client
Segmentation RFM analysis, Churn probability Survival Analysis and ETA for delivery services Linear regression, XGboost, Deep Learning and KNN.
Tools: SQL, Python and R
Development of classification models Logistic regression, Decision tree and
Random Forest oriented for churn.
Tools: SAS, SQL and R
Development of models focused on understand behaviour based on
geolocation and sociodemographic variables. The main focus was in cluster analysis to predict buying propensity in home broadband.
Tools : SAS, R and Microsoft Excel
I worked as an intern providing support to the consultants in projects in different segments such as services, oil and gas and chemicals.
This MicroMasters program in Statistics and Data Science was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). This program brings MIT’s rigorous, high-quality curricula and hands-on learning approach to learners around the world. It focuses on big data and making data-driven predictions through probabilistic modeling and statistical inference; identifying and deploying appropriate modeling and methodologies in order to extract meaningful information for decision making
Nanodegree focused in understanding several machine learning techniques and applying them to a rich set of business problems.
-Graduated in 2013, in 4 years, at Unicamp as the 8th top student (total of 74)
-Coursed several disciplines to improve my skills in modeling and to better understand the applications of statistics in economics: Econometrics, econophysics, stochastic process, demographics
-Developed a scientific research in statistical quality for a year ( 2011 - 2012) with a PIBIC scholarship: "Monitoramento e controle de atributos de pequena probabilidade de ocorrência usando as estatísticas p e g : uma análise de performance"