ABOUT ME

Professional Experience

[2017/2023]
Trader, Self Employed

• Acquired in-depth knowledge of operational methodologies, technical analysis, and market context to develop a successful trading strategy.

• Developed successful trading strategies based on in-depth knowledge of operational methodologies, technical analysis, and market context.

• Identified key relationships between geopolitical events, economic cycles, and global capital market influences for informed investment decisions.

• Actively monitored a wide range of economic indicators and market developments to make data-driven trading decisions.

• Demonstrated strong analytical skills and attention to detail, resulting in consistent trading performance and a solid understanding of market dynamics. 

[2015/2018]
Data Analyst - Costumer Lifecycle, Itaú Unibanco Bank

• Led the creation of the bank's first customer life cycle coordination, focusing on acquiring, retaining, and building loyalty among clients.

• Utilized SAS, SQL, and MicroStrategy platforms to collect and analyze data for the CRM database, providing insights and support to other teams.

• Managed campaign results, negotiated deadlines, and reviewed marketing materials for efficiency and effectiveness.

• Utilized Excel and Power BI dashboards to monitor, report, and request goals from operators and regional managers. 

[2013/2015]
Cross Selling Products Intern, Itaú Unibanco Bank  

• Contributed to the development of sales targets for car financing, ensuring alignment with business goals.

• Participated in the creation of promotional campaigns, using data-driven insights to inform marketing strategy.

• Monitored the performance of banking competitors in credit provision, identifying trends and opportunities for growth and improvement.

• Developed automated reports in Excel and MicroStrategy and created Tableau and Power BI dashboards for presentations to management and executive board members.

Educational Background

[2021/2022]
Data Science & Big Data MBA, FIAP
Machine Learning Algorithms: Predictive Modeling, Multiple Linear Regression, Logistic Regression, Cross Validation, Penalized Regression, Process Estimation and Discrimination, Text Mining, OSLR, Ensemble Methods, Adaboost, Neural Networks and Deep Learning;
Languages: Python, R;
NoSQL: Redis, Cassandra, MongoDB, Neo4J, Hive/HBase;
Relational Bases: Oracle and Vertica;
Semi-structured data: ORC, Parquet and AVRO;
On Premise: Hadoop, Spark,
On Cloud: AWS, Azure;
Data integration: Flume, Sqoop, Kafka, Nifi and Spark;
Data Mining: Apriori, PCA, KNN, Kmeans, Decision Trees, SVM and PageRank.
* Extension in Transformative Technology & Exponential business development

[2011/2015]
Undergraduate in Business Administration and Supply Chain, ESPM 

Academic Excellence Award “Feasibility of a new business Plan” - FINAL PAPER; 

Academic Excellence Award in the Discipline of Marketing IV;

Award for the best academic work developed to Samsung - ESPM / SAMSUNG.

[2020/2021]
Data Scientist Training Course 428h, Data Science Academy
Big Data Analytics with R and Microsoft Azure Machine Learning;
Big Data Real-Time Analytics with Python and Spark;
Data Engineering with Hadoop and Spark;
Machine Learning Algorithms;
Business Analytics;
Data Visualization and Dashboard Design;
Data Scientist Career Preparation;
Microsoft Power BI for Data Science.

[2020/2021]
Business Intelligence Training Course 228h, Data Science Academy
Econometric analysis in R;
Financial and capital markets;
R for data analysis;
Statistical data analysis;
Machine Learning Algorithms in R.

[2020/2021]
Statistics for Data Scientist Training Course 220h, Data Science Academy
Statistical analysis I and II for Data Science;
Mathematics applied to Machine Learning.

[2020/2021]
SQL for Data Science 64h, Data Science Academy
Queries, filter, ordering, operators, categorization, coding, binarization of variables, joining tables, aggregating data for analysis, window functions, subqueries, treatment of dates, exploratory data analysis, cleaning and data processing, SQL programming, SQL query optimization, Google Data Studio. 

[2020/2021]
Data Science Training Course 102h, Alura
Linear Regression: Advanced Modeling Techniques;
Statistics I: Understanding Data with R;
Statistics II: Deepening into Hypotheses and Correlations;
Access: Queries with Grouping and Classification;
VBA: Automating Tasks in Excel;
Data Analysis: Introduction to Analysis with R;
Tableau Dashboard: Data Visualization and Analysis;
Python Pandas: Handling and Analyzing Data;
Python for Data Science: Introduction to the Language and Numpy;
Python for Data Science: Functions, Packages and Pandas;
Statistics with Python I: Frequencies and Dispersion Measures;
Statistics with Python II: Probability and Sampling;
Statistics with Python III: Hypothesis Tests. 

[2020/2021]
Data Science Training Course 96h, DataCamp
Introduction to Python: Python interface and popular packages;
Intermediate Python: Creating visualizations using matplotlib and DataFrames with Pandas;
Data Manipulation with Pandas: Importing and cleaning data, visualizations with Pandas;
Joining Data with Pandas: Combining data from multiple tables;
Introduction to Statistics in Python: Collecting, analyzing and drawing conclusions;
Introduction to DataViz with Seaborn: Creating informative visualizations in Python;
Introduction to NumPy: Learning how to create, sort, filter and update arrays;
Python Data Science Toolbox: Writing functions in Python. 

[2022/2023]
Excel Immersion 127h, Hashtag
Introductory Concepts, shortcuts; formatting, basic functions; sort and filter; data tools; main labor market functions; dynamic table; graphics; text functions; date functions; financial functions; prediction and solver; protection and printing; Impressive dashboards; tricks and tools; macro recording; Power Query; Power Pivot; new Excel and Google Sheets functions.

Skills

• Microsoft PowerPoint 

• Microsoft Excel

• Jupyter Notebook

• MicroStrategy

• Statistics

• Python 

• Pandas 

• SQL 

• SAS

• R

• Excel

• Tableau Power BI