Veronica Mata Ramirez

Veronica Mata Ramirez has a diverse work experience, starting with their current role as a Machine Learning Engineer at AdVon Commerce since April 2021. In this position, they develop ML/AI software solutions for E-Commerce, including content generation through transformer-based models and the tuning and deployment of NLP models in GCP. Veronica also creates Streamlit apps to automate AI processes.

Prior to their current role, Veronica worked as a Data Scientist at Apli from September 2019 to April 2021. Veronica'sresponsibilities included building, training, and deploying ML models with AWS. Veronica also analyzed the psychological profile of candidates to estimate their suitability for specific job positions and worked on document OCR using Amazon Textract.

Veronica also has experience as a Co-Founder and Data Scientist at Synapsta from January 2019 to August 2020. Additionally, they worked as a Data Analyst at salauno from December 2017 to January 2019, where they developed machine learning models, created Power BI dashboards, and performed data analysis using SQL and Python.

In their early career, Veronica held various positions at salauno, including Systems Analyst, where they developed an online agenda using PHP and Lumen, and Information Technology Intern.

Veronica Mata Ramirez pursued their education in Computer Systems Engineering at Instituto Politécnico Nacional, where they obtained their Engineer's degree from 2014 to 2018. Additionally, they had an internship in Telecommunications Engineering at Universidad Politécnica de Madrid for a period of 6 months between 2016 and 2017.

In terms of additional certifications, Veronica has completed the course on Neural Networks and Deep Learning from Coursera in October 2022. Veronica also obtained certifications in Emprendimiento from Santander Universidades and Google Analytics from Google Ads in February 2019. However, the information regarding the certification in Business Analytics from The Wharton School lacks specific details on the obtained month and year.