Professional Experience

During my Post-Grad in AI & Data Science at Loyalist College, I plunged into a whirlwind of hands-on labs, hackathons, and research collaborations. I fine-tuned BERT and Transformer models for text classification, built CNNs and GANs in deep-learning workshops, and containerized end-to-end ML services with Flask and Docker. I orchestrated PySpark data-preprocessing pipelines on large financial datasets, visualized key insights in Tableau, and even led a mini research team exploring ethical implications of language models at AIGS meetups. All of this culminated in my capstone: architecting the RAG layer of a Financial Q&A chatbot that lets investors literally "talk" to their reports—seamlessly bridging raw data, embeddings, and real-time retrieval into one elegant pipeline.

At Contentstack, I transformed disparate data feeds into a single source of truth. I built SQL + Fivetran ETL pipelines into Snowflake, then built interactive dashboards in Tableau and Zoho Analytics—slashing report-generation time by 30%. By partnering with product, marketing, and engineering teams to define and enforce clean data governance, I turned raw numbers into strategic, board-ready insights that steered product roadmaps.

I completed a rigorous curriculum covering the core mathematics behind data science—probability theory, statistical inference, linear algebra, multivariate calculus, differential equations, and numerical optimization. My honors thesis applied multiple regression and time-series models to predict academic performance with 85% accuracy, while team projects used matrix decompositions and Monte Carlo simulations to tackle real-world problems. This solid mathematical foundation laid the bedrock for my AI and machine learning work.