In charge of the analytical processes at Ciudata, a startup focused on urban data and spatial analysis- Conducted spatial data science analyses, including brand coverage assessments and building connectivity evaluations (hospitals, schools, ATMs).- Dashboard development to visualize over 100k POIs (points of interest), providing actionable insights into urban data trends.

- Contributed to research projects regarding machine usage data stored on TimescaleDB, addressed data management issues, and improved Grafana dashboards to enhance performance while generating equipment use reports

- Dashboard development shared with diverse clients across various sectors, including Industrial Production, Retail, Marketing, and Telecommunications.- Developed a data-driven methodology for inferring socioeconomic level indicators from a sample of 1000 surveys in Paraguay. Leveraged Principal Component Analysis (PCA) and statistical techniques to identify key survey features, and applied clustering algorithms to group respondents into distinct socioeconomic categories. Implemented a point-based method using relevant survey questions and responses for accurate assignment to socioeconomic levels.- Performed semantic clustering on open-ended survey responses, through text vectorization, feature reduction, and Hierarchical clustering techniques.- Modeling and forecasting product demand with a track record of forecasting cement- demand and modeling real estate market cycles.

- Data analysis on district heating systems using MATLAB and Python, emphasizing data qualityassessment and pattern analysis.- Applied Time Series Analysis and Modeling techniques to predict heat load demand effectively. Including statistical methods like SARIMAX, VAR and H3 indexing; and Machine Learning methods like MARS, Neural Networks, and SVM- Research on consumption pattern-based methods to implement customer segmentation and enhance system efficiency, including Dynamic Time Warping (DTW) and wavelet signal filtering.