I collect and manage metrics and HR data from a variety of sources (Spark, SAP HANA, SQL, Excel), payroll results, management and employee surveys, exit interviews, job records, headcount, selection, talent and other sources.

I analyze data and statistics in search of trends and patterns with attention to recruitment, hiring practices, motivation, turnover and talent development. Using these analyses I generate machine learning models of rotation and talent development

I manage human resources dashboards. Manage metrics of selection, development, talent and other areas

I have made different proposals for the improvement in the management of the Spark, Teradata and Oracle databases that are used for the generation of risk management models

Designed and built processes for data integration and visualization using Tableau, PowerBI, Teradata and Python

Using Python I generated models for admission and risk behavior.

When analyzing the different databases, I proposed initiatives that allowed to improve the operation and management of the store to deliver a better customer service. (Customer profiling, cluster d and after-sales consultants, Store Scoring)

Using the CRISP-DM and Python methodology, I created models that allowed to find patterns that improved productivity in the sales that were made in the stores. In addition, I generated a model for the prediction of visits in stores nationwide

I proposed and developed business intelligence cubes that allowed to deepen the analysis of the main indicators of Movistar stores

Using Power BI, SQL, R generated dashboards for store management

Through the use of the products offered by Oracle EPM allowed me to propose business intelligence models that provide value to the business. In this way I developed models that allowed to control the operability within the distribution center of the products at a level of minimum detail (SKU). This allowed greater control of the costs incurred by each of the business lines in each in the warehouses and processes within the distribution center

With the use of BI models I was able to propose improvements in the sales and costs of the lines for each season, in which I used Python to process the most efficient data and expose a conclusion.