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Data CollectionResearch Intern, Cybersecurity Analytics (Red Delfín | Scientific and Technological Research Pacific Summer Program (XXV. Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Python, SQL, and scikit-learn. Education includes Bachelor of Engineering, University of Research and Development (UDI) (2024). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Classification, Data Collection, and Evaluation.
TextData Collection-Defined the basis for AIS-based vessel classification and operational phase detection; analyzed more than 150,000 trajectory points using heuristic and statistical rules. -Tested an LSTM-based activity recognition model (IMU data, man standing still, fallen, SOS, running) with digital filtering, sliding windows, and real-time inference. Explored Transformer variants and optimized class-balanced datasets for supervised training.
Data CollectionAnalyzed large-scale educational assessment data from Saber PRO using SQL and Python to support downstream reporting. Cleaned and transformed records, handled outliers, and prepared data for visualization of trends by demographic segments. Communicated results through a Looker dashboard to inform education policy recommendations. • Dataset included more than 150,000 exam records (2015–2022). • Applied data cleaning, transformation, and outlier treatment workflows. • Produced a Looker dashboard for score trends by gender and socioeconomic segment. • Presented findings to support educational policy recommendations.
Built KPI dashboards and delivered recurring performance insights to support marketing decision-making. Automated reporting workflows and translated dataset results into actionable audience segmentation and content optimization recommendations. Visualized metrics using Excel and Power BI for two brands. • Created KPI dashboards in Excel and Power BI. • Reduced reporting time by 40% through dashboard/report automation. • Delivered monthly performance insights and optimization recommendations. • Focused on measuring and evaluating marketing performance metrics.
ClassificationBuilt a Random Forest classifier to detect intrusion patterns from university network logs, including port scanning, DoS, and brute force attacks. Performed feature processing and model development in Python using SQL-derived datasets. Evaluated classification performance to quantify accuracy for security analytics use cases. • Input data consisted of network log records (structured text logs). • Used machine learning for supervised classification rather than manual labeling. • Trained and tuned a Random Forest model for intrusion detection. • Reported an 85% accuracy result for the trained classifier.
Bachelor of Engineering, Systems Engineering
IT Operations Engineer and Python Backend Engineer
Research Intern, Data Analysis
