I am a junior data and machine learning professional with hands-on experience in Python, SQL, analytics, quality assurance, and automation. I am completing an Honours Bachelor of Science in Astronomy and Physics at the University of Toronto, where I have built a strong quantitative foundation through coursework in algorithms, optimization, statistics, databases, and software design.
I have experience developing and validating datasets for AI systems, evaluating model outputs, identifying recurring errors, and producing structured quality-assurance reports. My work has involved processing hundreds of code snippets, conducting peer reviews, and helping improve data quality for enterprise model training.
I build automation and web-testing solutions using Selenium, Python, and web-scraping techniques. I have helped automate usability and bug-testing workflows, tested website features, contributed to React-based websites, and collaborated with clients on long-term product feature roadmaps.
My data science experience includes clustering, market analysis, ETL pipeline development, time-series data processing, and machine learning classification. I have used k-means clustering for user categorization, analyzed competitive markets, and translated complex data into actionable business recommendations.
I am particularly interested in applying data engineering and machine learning to real-world problems in digital health, analytics, and scientific research. My projects include an ML-enabled therapy application and an astronomical variable-star classification pipeline using large-scale GAIA DR3 and TESS data.
