I am a results-oriented Data Scientist holding an MSc. in Applied Statistics and a BSc. in Applied Mathematics with over 8 years of experience bridging the gap between statistical theory and technical execution. I specialize in designing advanced quantitative and predictive modeling solutions to solve complex challenges across finance, healthcare, and education.
My expertise spans the full data lifecycle, from architectural groundwork to actionable insights:
Cloud & Data Engineering: I have extensive experience optimizing enterprise data management and cloud migrations on Azure (Databricks, PySpark) and AWS. I architect robust ETL/ELT pipelines, manage data lakes using Hadoop and Hive, and utilize SQL and Bash for high-efficiency data processing. At TD Bank, OI and CDC, my optimizations directly contributed to a 20% boost in processing efficiency.
Machine Learning & AI: I leverage Python, R, SAS, and TensorFlow to build sophisticated models, including NLP (Natural Language Processing), fraud detection, and customer churn prediction. My work in optimizing database schemas and predictive algorithms has delivered measurable results, such as a 15% increase in model accuracy.
Business Intelligence & Leadership: I bridge technical and business teams by translating complex data into strategic decisions through Tableau visualizations. I thrive in Agile (SCRUM) environments, acting as a Subject Matter Expert (SME) to lead code reviews, mentor peers, and ensure rigorous QA and regulatory compliance.
In addition to my corporate work, I am a published researcher with a strong academic foundation in Bayesian hierarchical modeling and global health statistics. I combine this rigorous academic background with technical fluency to drive innovation, data integrity, and measurable business value.