Alexandre C.
Data Scientist

Skills

Spark

Github

Microsoft Sql Server

Python

Microsoft Azure

Amazon Aws

Docker Cloud

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Bio

Graduate in Computer Engineering from the Federal University of Paraíba (UFPB), currently employed as a Data Scientist. Possesses over two years of experience in the field of data science, showcasing an ongoing commitment to acquiring diverse, market-relevant skills. Recognized for proactivity, empathy, and the ability to swiftly learn and adapt to new subjects and technologies.

Data Scientist
7/1/2022 - 1/1/2024

Built predictive data models and solutions to assist internal company decisions, expanding expertise in Python, PySpark, SQL, Azure cloud services (Databricks and Azure DevOps), GIT, and PowerBi. Executed a Product Similarity project using machine learning clustering techniques with the objective of reducing product shortages in stores by replacing them with similar items, leveraging Python (Numpy, Scikit-Learn), PySpark, SQL, GIT, and Azure services. Developed tools to add business intelligence aimed at improving internal processes and achieving efficiency and cost reduction, utilizing data ETL, SQL, PySpark, GIT, Azure, Business Intelligence (business indicators), and PowerBi. Optimized the replenishment flow of stores through a product scoring system based on sales performance, aiming for enhanced sales through better logistics, employing Python (Numpy, Scikit-Learn), PySpark, SQL, GIT, and Azure services.
Data Scientist
1/1/2020 - 7/1/2022

Developed tools to automate internal processes targeting fraud prevention in payment methods, including data modeling for internal scoring using Classification and Regression Algorithms to lower operational costs. Executed backtests on potential partners' databases and performed analytical data extraction by structuring indicators to enhance team workflows. Utilized robust statistics to detect behavioral anomalies in banking transactions with proficiency in Splunk, ISO 8583, and Outliers. Created comprehensive dashboards for visualizing trends and grouping fraud incidents using Metabase, Power BI, Python, and SQL Server. Optimized FICO Falcon system rules leveraging Python, Isolation Forest, Random Forest, and K-Means algorithms.