Junior Data Scientist

Evaluates and improves large-scale AI model outputs through rigorous data quality analysis and root cause identification. Helped reduce systemic errors by approximately 30% across evaluation cycles, supporting model performance optimization.

Detects anomalies and behavioral patterns in sequential model outputs using structured analytical frameworks. Identifies data inconsistencies proactively before they affect downstream results.

Translates technical requirements into structured data-improvement instructions, maintaining data consistency and integrity in more than 90% of evaluated cases and supporting reliable, scalable AI training pipelines.

Built and trained a computer vision model with OpenCV, TensorFlow, and Keras for object detection and classification. Applied feature engineering and data augmentation to image sensor data and achieved approximately 85% test-set accuracy.

Designed an end-to-end ETL pipeline using Python and SQL to consolidate, clean, and transform data from heterogeneous sources. Reduced data preparation time by approximately 40% while establishing a single source of truth.

Developed machine learning predictive models to identify patterns and support decisions, presenting results through interactive Power BI dashboards. Also implemented a Microsoft Copilot Studio automation workflow that reduced repetitive manual work by approximately 35%.

Ninth-semester undergraduate program in progress.

Technical bachelor's degree in Business Administration.