Daniel José Ortiz Abauat

I’m a Data Scientist with a geoscience background and hands-on experience building ML pipelines for real-world forecasting and operational decision-making. I bring a practical, production-minded approach with Python, SQL, PySpark, and AWS—backed by an AWS Certified Cloud Practitioner credential.

At Corporación Autónoma Regional del Alto Magdalena – CAM, I designed data collection and validation protocols for geospatial datasets, applying ETL and standardizing structures that reduced logging errors by ~30% across 5+ municipalities. I also automated reporting pipelines using Python, cutting manual documentation time by ~40% and freeing capacity for analytical work.

In parallel, I translate complex geological metrics into clear, actionable reporting for territorial planning and high-stakes risk management under critical deadlines. Earlier, during my Geology Internship at Agencia para la Infraestructura del Meta – AIM, I supported multitemporal hazard-zone analysis using statistical modeling and data mining, and I strengthened data governance through structured contractual documentation.

For my Master’s Thesis (DATAI4), I’m building energy demand forecasting models for industrial optimization using regression, feature engineering, ARIMA, and LSTM on large energy datasets. I’m focused on bridging technical forecasting with measurable business KPIs—so models don’t just perform, they drive operational outcomes.