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Víctor Macedo Becerril

Víctor Macedo Becerril

Data Scientist
México, México

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Sobre Víctor Macedo Becerril:

I am interested in data science and machine learning algorithms in order to find interesting insights. In addition, I am interested in multi-objective evolutionary computation to model and solve applied optimization problems.

Experiencia

Head of Data Scientist - Go Sharp
4 people in my charge. Teamwork with other areas and with the client Retail: Multiple
simultaneous projects (on average 5). Exploratory analysis, proposal of solutions and
exposure through visualizations. Creation and improvement of ML models using Time
Series, Clustering, RFM analysis, XGBoost, association rules, BTYD models, etc., for
customer classification and sales inference. Use of servers and tools R, Bash, SQL,
git, Jira, AWS, python.
Data Scientist Coordinator - Kaloni
3 people in my charge. DB Administration: Creation and improvement of DB to dis-
play data on web page. ETL: From a CRM (NetSuite), extract data of interest through
ODBC (SQL) and transform it using R, saving the processed data in a DB; automated
process to be done every 5 minutes using a server using a bash. BI/EDA: Creation
of dashboards in Shiny/Flexdashboard and Power BI for the visualization of analyzes
and reports. KPI's: Analysis of leads and customers (ages, entry time, location, etc.),
calculation of times for each stage of the value chain, analysis of information to find
errors such as missing data or data that does not correspond to the field, contact anal-
ysis center and up-to-date monitoring in the making of medical records, etc. Analysis:
text mining for emails, sales forecasting, association rules, etc.

Educación

Master in Process Modeling and Optimization - CIMAT
Areas: Math Modeling, Optimization, O.R., Code Developing, Scientific Computing,
Statistics, Machine Learning.
Thesis:”Mathematical Optimization Techniques for a Congregation Assignment: a
case of study”. Minimize dispersion among personnel and minimize distances to cen-
ters, generating Pareto front approximation using NSGA-II. Resolution of the mathe-
matical model with CPLEX and metaheuristics. C++ code implementation.
Advisors: PhD Jonás Velasco & PhD Adriana Lara.

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