Azaki | Résumé

Education:

University of Toronto — BAS Industrial Engineering

2015 - 2020

PEY Co-op Program
Dean's Honour List
Minor in Artificial Intelligence

Skills:

Python

JavaScript

Java

HTML

CSS

Node

React

Git

SQL

Firebase

PyTorch

MATLAB

TensorFlow

Experience:

Cancer Care Ontario — Full Stack Developer

Aug/19 - Apr/20 Toronto, ON

Developed a full-stack web application to create, share and receive feedback on medical forms in line with Synoptic Reporting standards; reducing total development time of a new form from 12 to 6 months
Replaced tedious data collection and aggregation steps (emailing Excel/Word files back and forth) with online feedback forms powered by a Firebase backend; saving 20 hours per week of manual work

Procter & Gamble — Software Engineer

Oct/18 - May/19 Toronto, ON

Built an end-to-end development pipeline in Java that automatically detected software changes and published to server; reducing deployment time by 98%
Developed new plugins in Java for an open-source analytics software (KNIME) that leveraged Google and Facebook APIs to automate recurring tasks; saving on average 150 hours per week (30 users) and reducing human error rate by 20%

Procter & Gamble — Web & Search Analyst

May/18 - Oct/18 Toronto, ON

Analyzed web traffic data in Google Analytics to assess performance of marketing strategies and suggest improvements; eliminating low-value advertising channels and increasing advertisement interaction by 15%
Automated web page performance audits using JavaScript and collaborated with brand teams to implement recommendations; increasing overall website traffic by 20% and improving Google Search rankings by ~2

Projects:

SmartyPants — JavaScript/Java/SQL

Created a full-stack movie recommender system using JavaScript/HTML/CSS for the frontend and Java Server Pages (JSP) + JDBC/SQL for the backend
Users begin by rating a few movies, then a predictive algorithm extracts key features and patterns to generate movie recommendations

eSports Betting — Python

Developed a machine learning model in Python to predict the outcome of professional Counter-Strike games with 62% accuracy on unseen games
Created an open-source betting assistant that combines the model prediction with current commercial betting odds to suggest the recommended bet

The Federalist Papers — MATLAB

Developed a support vector machine (SVM) in MATLAB that classified authorship of the disputed federalist papers using a non-probabilistic linear classifier
Implemented two separate algorithms to solve the classification problem with < 0.2% error
Consolidated and visualized results including algorithm comparison, significant features, and hyperparameter optimization

Leadership / Volunteering:

HiSkule Mentorship

Student-led initiative with the goal to encourage high school students to pursue STEM related careers by providing personal guidance and support

Hack the North 2018

Attended Canada’s biggest Hackathon as a sponsor representing Procter & Gamble with the goal of engaging students and assisting with recruitment

Go North Youth 2019

Hands-on activities, demonstrations and workshops designed to ignite curiosity about STEM; partnered with Google and Actua

Saturday Program

Non-profit tutoring and mentorship program that helps high school students in Toronto by pairing them with university students from different faculties at the University of Toronto.