Britney Saw

About Me

Hi, I’m Britney!
I recently graduated from the National University of Singapore with a degree in Data Science and Analytics.

I’m a curious, motivated learner who values a balance between fun and personal growth. Right now, I’m working on:

A Tableau dashboard visualizing expenses from my six-month exchange program in the Netherlands.
An interface to help people find car parks around Singapore.

In my free time, I enjoy cooking, exploring musical instruments, and most recently, I’ve been training for a marathon.

Work and Education

Download My Resume (PDF)

Here’s an overview of my education, work experience and projects:

Education

National University of Singapore (NUS), 2025
Bachelor of Science (Hons) in Data Science and Analytics

• Recipient of NUS Merit Scholarship
• Dean’s List AY22/23 (Top 5% of cohort)
• Courses Taken:
   Introduction to Data Science (A),
   Data Visualisation (A-),
   Numerical Computation (A),
   Data Structures and Algorithms (A-),
   Regression Analysis (A-),
   Machine Learning (A-),
   High Dimensional Statistical Analysis (A-),
   Case Study in Business and Commerce (A-)

Eindhoven University of Technology (TU/E), Netherlands, 2024
Student Exchange Program in Mathematics and Computer Science

• Master level modules in Data-Driven AI and Time Series Forecasting
• Research on stochastic processes to model emergency response and optimize deployment strategies
• Achieved 9.5/10 in Financial Mathematics

Work Experience

Data Science Intern at Holmusk, Aug 2024 – May 2025

Holmusk is a healthcare analytics company specializing in real-world data-driven solutions.

Key contributions:
• Optimized over 100 Python and R functions to enhance SQL query performance and improve overall user experience on Holmusk’s analytics platform
• Automated and maintained technical documentation using shell scripting and the Sphinx library, boosting platform maintainability and updating the GitHub Wiki
• Refactored internal data pipelines using PySpark on Databricks to improve processing and scalability
• Streamlined package setup processes by reducing package setup time, boosting operational efficiency for development teams

Projects

Fraud Detection in Telecom Networks using Graph Neural Networks, Feb 2025 - Apr 2025

• Designed a GNN-based fraud detection system on multi-million record telecom data during a collaborative 8-week hackathon, processing and engineered graph-based features to model user interactions and detect fraudulent behavior
• Conducted exploratory data analysis (EDA) and anomaly detection to identify fraud patterns across voice calls, SMS and application usage, which informed graph construction for modeling
• Tuned ensemble models (CARE-GNN) to achieve high classification performance (AUC 0.9534) for fraud prediction

Carpark Demand Simulation at NUS, Aug 2023 – Nov 2023

• Led a 4-person team to build and containerize an interactive R Shiny application using Docker, integrating a discrete-event simulation model to optimize allocation across 6 NUS carparks
• Created a simulation dashboard with data visualization to support agile operational decision-making for sudden surges in parking demand
• Presented data-driven recommendations and translated technical insights into actionable parking system optimization strategies to non-technical stakeholders for decision making
• Delivered a user-friendly and visually appealing UI, earning positive feedback from stakeholders