Engineer & Data Scientist —
I build end-to-end data products with Python, SQL and Power BI — from ETL and predictive models to interactive apps in Streamlit. Currently preparing the Google Professional Machine Learning Engineer certification.
I transform raw data into clear decisions. I enjoy tackling real problems with practical, production-minded solutions: data pipelines, robust models, automated reporting and intuitive dashboards. My background in mining engineering gives me a structured, results-driven approach; my training in Data Science lets me apply statistical rigor and ML to business needs.
- Languages: Python, SQL
- Data & ML: Pandas, NumPy, scikit-learn, XGBoost, TensorFlow (basics)
- BI & Visualization: Power BI (DAX, Power Query), Matplotlib, Seaborn, Streamlit
- Data engineering / DBs: ETL, SQLAlchemy, PostgreSQL, MySQL
- Dev & infra: Git, GitHub, python-dotenv
- Other libs: OpenCV, ipyleaflet, ipywidgets, sympy
Asteroid Collision Prediction — github.com/PazReumante/asteroid_prediction
End-to-end predictive solution using Python & SQL: ETL of NEOCC (ESA) data, supervised modeling (92% test accuracy with scikit-learn & XGBoost), and an interactive Streamlit app for trajectory and risk visualization. Published as an open-source project.
- Bachelor's in Mining Engineering — Technological University of Chile (Oct 2019)
- Data Science & Machine Learning — 4Geeks Academy (July 2024)
- IBM — Generative AI for Data Scientists (certificate)
- Google Professional Machine Learning Engineer — in progress
- Ability to translate business questions into data pipelines and actionable ML models.
- Practical experience in real estate operations, financial reporting and automated dashboards.
- Strong focus on data quality, reproducibility and delivering insights that drive decisions.
- Collaborative mindset, curiosity and continuous learning.