Three essential things about my ideology: I know that the most important asset of a company is data, technology is to help people and data science is an art.
I have worked as a data scientist both in the private sector and in the research field with more than 3 years of experience, with the ability to develop and manage projects using ML and statistical techniques, as well as tools and software such as Python, Power BI, Azure among others, my academic background is as an economist, with a focus on finance, econometrics and sustainable economics.
I like to be able to interpret reality through numbers to find answers that help empower businesses and people. My goal is to find solutions and contribute to business development, as well as to contribute to technological development worldwide.
Learning more about AI, ML and deep learning because the future is to opt for solutions focused on these types of technologies.
09.2022 - Currently
Process automation with SSIS.
04.2022 - 09.2022
Database maintenance and ML to improve user experience.
05.2021 - 11.2021
Econometric analysis and model testing (A/B testing).
01.2020 - 07.2020
Econometric time series analysis.
06.2019 - 12.2020
ML for sales model prediction.
Finalist in the top 5, focused on retail companies to know the level of customer satisfaction, created with azure cognitive services, capable of recognizing emotions and generating graphs so that the customer can understand the behavior of their customers.
I was able to demonstrate the impact of financial education on the Mexican economy. I obtained a certificate of ethics in the handling of sensitive data granted by the university where I conducted the research.
Developed with Python to know the current state of a movie catalog, using native libraries and experimenting with BeautifulSoup. The next step is to create an analysis with ML to predict which movies are the most interesting for the audience.
Personal project to recreate the way I talk to people. It consist of three stages, with the first one being developed: training with over 40.000 data. The second will be improving the quality of data, increasing to 120.000 data.
Skills
Python
NumPy
Pandas
Scikit-learn
C#
SMS
Jira
Git
Azure
Power BI
Certificates
