About Me

I graduated from York University with an Honours Double Major in Applied Mathematics and Economics in December 2023.
During my undergrad, I took part in courses such as Data Analytics and Machine Learning, Stochastic Processes, PDE/ODEs, Mathematical Finance, and Linear/Combinatorial Optimization.
I was also a Team Lead for Google Developer Student Clubs @ York University, and a Head Organizer for yuHacks 2022 Online Hackathon, where I picked up my analytical and mathematical thinking skills to apply onto real world situations, and leadership skills.

I am most experienced in SQL, R, and Python with a track record of leading successful tech initiatives. I like to translate data insights into strategic actions.

I am now looking for an entry level position or an internship position as a Data Analyst/Scientist.

Leadership Experiences

During my time as Club Lead of Google Develoepr Student Clubs @ YorkU, I led a team of 6 talented staff members to deliver a cool experience where York University students could collaborate with other computer science clubs and industry professionals in tech through the use of Google Cloud Platform.

As the Head Organizer of yuHacks 2022 Online Hackathon, I delivered a 150+ participant online hackathon that facilitated discussions with tech leaders, enhancing networking opportunities for the hackers, and hosted workshops on data analytics applications.

Projects

Here are some projects that I'm proud to share.

Statistical Arbitrage with Monte Carlo for $NVDA American Options Pricing

Performed ETL on Nvidia options data by using interest rates and historical American options data from the Chicago Board Options Exchange (CBOE), executed Monte Carlo simulations (n = 1,000,000) for each options listed between 01/01/2023 to 12/31/2023.

Exploratory Data Analysis of Credit Card Fraud Detection dataset and kNN classifier with SMOTE

Developed a kNN classifier in Python with sklearn library to perform predictions on whether a credit card usage is fraudulent on a Kaggle dataset (n=284,807).


E-Commerce Customer Segmentation Analysis with RFM and K-Means Clustering

Analyzed a ~1 Billion-record Brazilian e-commerce dataset by leveraging SQL queries to aggregate customer transaction data; performed RFM (Recency, Frequency, Monetary Value) in R, identifying customer purchasing patterns for targeted marketing.

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