Resume
Education
National University of Singapore
School of Computing
BSc. in Business Analytics (Financial Analytics Specialisation in-progress)
Relevant Coursework
Aug 23 - Present
Singapore
Korea University
International Winter Campus
Participated in Korea University's Winter Campus academic and cultural immersion programme. Gained a strong foundation in core accounting principles and applied them to real-world business scenarios, demonstrating the ability to classify transactions and determine appropriate accounting treatments.
Relevant Coursework
Dec 24 - Jan 25
South Korea
Temasek Polytechnic
School of Business
Accountancy and Finance (Finance specialisation)
Relevant Coursework
Apr 18 - Apr 21
Singapore
Work Experience
Compagnie Financière Tradition Singapore (Returning in Jul 2026)
Quantitative Developer Intern
Quantitative Analytics (Financial Derivatives and Products)
Dec 23,
May 24,
May 25 - Present
Singapore
Skezi
Data Science Intern
Jul 25 - Dec 25
France, Paris
Portfolio
TheFairTracker I contributed to the development of TheFairTracker, a centralized grocery planning system built with Vue.js and Firebase. My key contributions include implementing the navigation bar with dynamic routing and search, developing the login system with password reset, and building key modules like Grocery Lists (view, add, duplicate, delete, rename, share) and Locations (searchable by supermarket). I also implemented functions to add, edit, and compute grocery item details, such as price and purchase status, and supported features for shared list access and favorites integration.
Muhammadiyah Welfare Home was a website development project for a Non-Profit Organization "Muhammadiyah Welfare Home", leveraging Vue.js and Firebase for real-time data and user authentication.
This BT4221 ML project explores loan default prediction using LendingClub’s dataset (2007–2020) through machine learning. The team performed extensive data cleaning, feature engineering, dimensionality reduction via PCA, and addressed class imbalance using SMOTE and hybrid sampling methods. Six models were evaluated, with Random Forest selected as the best performer (recall: 0.8261, PR_AUC: 0.1890). Important predictors identified include hardship_reason, grade, and home_ownership. Despite computational constraints, the team optimised resources effectively and derived practical insights to support LendingClub in enhancing risk management and implementing tiered pricing strategies.
Links: Code | Final Report
Smoodee is a startup project that focuses on providing consumers with healthy and nutritious yet made from blemished fruits and vegetables. I worked with Smoodee as a part of its operations and fund raising efforts. My team and I eventually secured $25,000 SGD from EB Impact that is supported by OCBC Bank, Meta Singapore's Ministry of Sustainability and the Environment, and City Developments Limited.
