- Implementing a variety of feature sets and solutions derived from using tools such as AI Large Language Models (LLMs), LangChain, recommendation systems, Natural Language Processing (NLP), and supervised learning models using AWS, Python, Docker, OpenAI API, and Hugging Face.- Conducting data analyses, and deriving meaningful insights from various data sources, through applying statistical methods, machine learning techniques, and artificial intelligence. Using tools like Python, LLM's, OpenAI API, LangChain, Supervised and Unsupervised ML, NLP, and visualization tools like Looker.- Providing talks, training, and mentorships in a range of topics including Artificial Intelligence, machine learning, statistics, data quality, data analytics, and data science.

- Design and implementation of methodologies based on the analysis of diverse data sources for multiple projects using SQL, Stata and Python. Documentation of processes and programming codes. - Creation of reports, Exploratory Data Analysis and data visualization for decision-making using Python, Power BI and ArcGIS. - Application of supervised and unsupervised Machine Learning models with Python for cluster identification, future behavior prediction, and comprehensive insights into the dynamics of the sector.- Design and generate financial indicators, leveraging Business Intelligence, to conduct performance analysis, track, gain insights, and make timely decisions.

Professor of Quantitative Methods at the School of Economics. I addressed different topics of economics, statistics, and industrial engineering, such as: the design of indicators, survey design and access to data sources, analysis and visualization in Excel and Tableau, regression models, logit, decision trees, statistical tests, time series, process optimization, project management, use of SPSS, among others. - I got a rating of 4.7 out of 5 from the students.

- Participate in the design, collection, and statistical analysis of the Innovation Culture Survey and the Organizational Culture Survey of the company, as well as the creation of an index using principal component analysis with R and SAS software. This analysis was used to characterize the company in terms of innovation and culture for decision-making at the management level.- Promote the use of Machine Learning, Big Data, and new technologies for the analysis and collection of statistical information, through training, incentives, and resource management, as well as the monitoring and support of more than 17 projects of different groups of the company.

- Elaborate and present the statistical analysis, report of results, design, validation, and documentation of the Technological Development and Innovation Survey and the ICT modules for households and companies. The processing of the indicators was optimized from 20 days to 1 week using SAS software.- Responding to requests from international and national organizations. Achieving goals and schedules.