Data Science - ML Engineering - AI Applications
Data Science & Data Engineering co-op candidate building analytics pipelines, machine learning models, and AI applications from messy data to deployable systems.
Master of Data Science and Artificial Intelligence co-op student at the University of Waterloo, with a Computer Science and Statistics background from the University of Toronto.
Technical focus
My work connects statistics, data engineering, machine learning, and full-stack application delivery, with projects acting as the practical evidence.
Ingest, clean, model, and document source data.
Explore features, train models, evaluate uncertainty.
Turn notebooks into repeatable APIs, jobs, and UI workflows.
Selected case studies
Each case study shows the problem framing, data work, modeling choices, results, and lessons learned behind the final artifact.
State-aware AI email assistant with gated tool execution
An AI safety-focused email agent using LangChain, LangGraph, prompt middleware, and human-in-the-loop controls to prevent unauthorized email actions.
LangGraph workflow for vendor sourcing, constraints, and text-to-SQL
A multi-agent coordinator that automates event-planning workflows with LangChain, LangGraph, LangSmith tracing, MCP tools, Tavily Search, and a self-correcting SQLite text-to-SQL agent.
Skill categories
Skill levels are grouped by applied proficiency, not percentage bars, so the work reads closer to how real teams evaluate capability.
Data cleaning, modeling workflows, notebooks, APIs, and automation.
Relational modeling, analytics queries, joins, normalization, and database-backed apps.
Cloud data engineering, warehouse workflows, and enterprise analytics pipelines.
Cloud data solutions for analytics, storage, and workflow integration.
Ship usable interfaces with feedback loops and guardrails.
Data cleaning, transformations, geospatial joins, aggregation, and EDA workflows.
Supervised learning, clustering, preprocessing, model selection, and evaluation.