John Alejandro Quintero Castillo
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John Alejandro Quintero Castillo

John Alejandro Quintero Castillo

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Detail

Data Scientist | Machine Learning & AI Projects | Python, SQL, Web Scraping | Cloud (AWS) in Progress
Cundinamarca, Colombia

Contact John regarding: 

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Full-time jobs
Starting at USD900/month
Flexible work
Starting at USD12/hour
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Internships
Open to unpaid internships
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Résumé

Jobs verified_user 0% verified
  • H
    Research and Development Engineer
    HOrigen SAS
    Nov 2020 - Mar 2023 (2 years 5 months)
    Key Achievements: • Developed a scaling mathematical model for electrocoagulation systems, enabling the expansion of wastewater treatment plants from 10 m³ to 200 m³. • Led the installation and commissioning of an industrial wastewater treatment plant (100 m³) for cheese production, utilizing electrocoagulation—an innovative technology in the country. • Designed an automated data collection and analysis system to monitor plant performance, implemented in VBA for generating daily and monthly reports. Responsibilities: • Conducted laboratory experiments to develop mathematical scaling models for electrocoagulation. • Created 3D models of industrial-scale electrocoagulation devices using Autodesk Inventor, optimizing equipment design. • Gen
  • H
    Technical Support Engineer
    HOrigen SAS
    Aug 2019 - Oct 2020 (1 year 3 months)
    Key Achievements: • Coordinated the construction, installation, and commissioning of a wastewater treatment plant and a potable water treatment plant based on electrocoagulation in oil drilling sites, with volumes of 20 m³. • Designed and implemented a prototipe of electrocoagulation waste water treatment equipment that reduced the residence time in 30%. Responsibilities: • Provided real-time remote support to field engineers, ensuring quick and effective resolution of operational issues. • Conducted applied research on electrocoagulation, evaluating key parameters to improve process efficiency. • Performed physicochemical analysis of water samples, contributing to the optimization of treatment processes. • Assisted in the implementation
Education verified_user 0% verified
  • T
    Data Scientist, Information and technology
    TripleTen LatAm
    Aug 2024 - Jun 2025 (11 months)
  • Australian College Of Business Intelligence
    Certificate IV Information Technology, TecnologĂ­a de la informaciĂłn
    Australian College Of Business Intelligence
    Feb 2024 - Jul 2024 (6 months)
  • Universidad Nacional de Colombia
    Grado en IngenierĂ­a, IngenierĂ­a quĂ­mica
    Universidad Nacional de Colombia
    Jan 2012 - Jan 2018 (6 years 1 month)
Projects verified_user 0% verified
  • B
    Bogotá Housing Price Prediction
    May 2025
    This project builds a machine learning XGBoost model to predict housing prices in Bogotá, Colombia, using data collected through web scraping from a real estate website. A total of 1,000 listings were scraped and processed, finding strong correlations between total area, stratum and price. In this project is developed a pipeline that takes raw data and gives price predictions.
  • I
    IndyCar Report Scraper
    Mar 2025
    🚀 Web Scraping of IndyCar Session Reports I developed a Python script to extract and organize data from IndyCar drivers' session reports. Using the pdfplumber library, the script processes PDF files, extracts relevant information, and structures it into Pandas DataFrames for analysis. 🔹 Automation: It can be executed from a Bash terminal or manually within a Python environment. 🔹 Application: Simplifies data collection and organization to analyze driver performance in each session. A project that combines web scraping, data processing, and automation. Link to Github repository: https://github.com/JohnQuintero08/indicar_scraping_pdf
  • P
    Predictive Modeling for Gold Recovery Optimization
    Feb 2025
    Developed a predictive model to estimate gold recovery in an extraction process, optimizing the analysis of operational data. Implemented data cleaning, segmentation, and feature extraction techniques to enhance model accuracy. Trained Linear Regression and Random Forest models, evaluated using sMAPE. Identified key patterns, such as plant shutdown periods, which improved data segmentation and boosted model performance. The Random Forest model achieved an sMAPE of 11.2%, demonstrating strong generalization capability. This analysis provided valuable insights into process efficiency and enabled the development of models that support data-driven decision-making to improve gold recovery. Tools: Python, pandas, numpy, scikit-learn, matplotlib
  • A
    Analysis of Deforestation Progress in the ChocĂł Department, Colombia
    Oct 2024
    This project analyzes deforestation data in the ChocĂł department, Colombia, between 2014 and 2021. Using Python, along with the pandas and matplotlib libraries, the data was processed and visually represented. Additionally, geopandas was used to extract the geographical boundaries of the area, obtained from the geojson.io website. The main objective was to represent the progression of deforestation reports and analyze the main causes and the most affected municipalities by this issue. Link to github repository: https://github.com/JohnQuintero08/deforestacion_choco