I manage a Data Science team composed of 15 people among leaders, Data Scientists and Machine Learning Engineers. Our mission is to provide effective and scalable analytical solutions to guide our clients toward smart decisions on a wide range of business problems such as go-to-marketing, geolocation intelligence for expansions and public relations. Some of our clients are: McDonald's, Heineken, Femsa, Adidas, Natura, Nestlé, Petrobras, Mercado Livre, Burguer King, Google, Seara, Oxxo, L'Oréal, Gol, Cacau Show, Americanas, Azul, Visa, Salesforce, Itaú, Microsoft and hundreds of others.As Cortex holds the largest investment in Big Data and Artificial Intelligence in Latin America, Data Science's deliveries are essential results for the success of the company and for our mission of transforming the path of people and businesses through intelligence.

Among many challenges, our main focus was to predict delinquency of school tuition. Not only were our models crucial for pricing and credit decision, but also for guiding the isaac's operation towards growth, profitability and scalability.

Our goal was to develop high quality models to support prices, communication, and collection policies. I also was the Product Owner of the Advanced Analytics Squad focused in new analytical solutions, such as Speech Analytics and new data, aiming the Collection Community OKRs.

I led a Data Science team focused in credit. Our mission was to improve all types of credit decisions and make Itaú-Unibanco a world's reference in customer-oriented services through Machine Learning and Big Data solutions.

I worked in the Digital Credit team supporting decisions with predictive models, processing huge datasets and building further Business Analytics solutions. I also helped other teams with Big Data Analytics in order to make Itaú-Unibanco a world's reference in digital credit and customer-oriented services. These are some of the tools that I worked with: Python, Hadoop, SQL, R, Bash, SAS and Office.

Besides working with research projects in Databases, Social Media Data and Bioinformatics, I workes in two big projects: InfoSAS and EuBra-BigSea. InfoSAS was a project by the Ministry of health of Brazil and hosted at UFMG to control the production by SUS (Brazilian public healthcare system). My main contribution was the implementation and analysis of a Machine Learning model for fraud detection, which is production at SUS.The EuBra-Bigsea is a project to build a Distributed System for Machine Learning. In addition to the project management, I worked in the data acquisition and case studies focused in Smart Cities.

During two months, I visited FernUniversität Hagen in Germany for a undergraduate program. My main duties were coding a Java system to simulate a peer-to-peer network and investigating the problem of churn in such networks.