I managed, developed and participated in the implementation of the Limpa Nome Collection Score Project, pioneering model used in more than 100 million debtors in Brazil to improve significantly the actions of Serasa Limpa Nome, which have an impact extremely positive for Brazilians and until then were not customized by risk profile.

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ML analysis to improve internal controls for mapping and assisting in the correction of systemic failures, serving the Revenue Assurance area, Vivo customer. ● Confection of advanced analyzes aiming at the creation of recovery strategies of revenue, using data science and Process management methodologies, such as PDCA ● Creation of Data Science analytical solutions to optimize decisions, products, services and processes; ● I acted as a pillar in Data Science in the Trilho Vivo project, under development and model evaluation. ● Development and evaluation of Machine Learning Models, through the application of advanced methodologies of Machine Learning and the use of tools open-source, as well as Python, R, Spark, among others; ● Process performance KPI's management; ● Data Governance and ML Process

● Making advanced analyzes to maximize results through the use of data; ● Creation of Business Policies & Strategies through Data Analytical Solutions Science to optimize decisions, products, services and processes; ● Negotiation with internal areas and external suppliers; ● Development and evaluation of Score Credit Models, collection, fraud, claims and propensity to adhere to products, through the application of advanced methodologies of Machine Learning and the use of open-source tools, such as Python, R, Spark, among others; ● Intelligent automation of the decision-making flow for granting credit, providing the maximizing results and reducing costs; ● KPI's management; ● Data Governance.

Responsible for modeling X-ray patterns and identifying their physical properties of samples. ● Use of the GSAS software to model the X-ray patterns of the samples, in order to to determine its physical properties; ● Synthesis of nanomaterials through a high pressure mill; ● Studies involving the behavior of matter on a manometric scale; ● Use of Raman spectroscopy to map the elements that make up the samples.

Presentation of the results of the modeling of X-ray patterns and identification of their physical properties from dust samples.

Use of machine learning (Statistical Modeling) to build strategies in order to increase the use and profitability of credit cards.

Presentation of data science results in companies like Nubank, Easy Taxi, among others.

Use of machine learning (Statistical Modeling) to build strategies in order to increase the credit card approval rate without increasing the risk (probability of default).