I am a strategic analyst experienced with techniques and best practices in predictive modelling, data discovery and data visualization. Professionally, I work at the Reserve Bank of New Zealand, as a new member of the Data & Statistics team.
Prior to my relocation to New Zealand, I worked in EY Singapore, as a member of the Forensics & Integrity Services team. We utilized a variety of tools to sift through databases, identify meaningful patterns and correlations with machine learning techniques, and translated them into actionable insights to drive business decisions.
Coming from a startup background, I’ve worn many hats in my career: programmer, writer, researcher, co-founder, social media strategist, mechanical designer and, marketer. As a result, I have a unique ability to manage multi-disciplinary projects and handle complex challenges. I love working with equally enthusiastic, cross-functional teams who are eager to build elegant and impactful products.
As an advocate for lifelong learning, I have developed and delivered workshops at my workplace, tutored polytechnic-level students, on the fundamentals of programming - SQL, R and Python.
Data Science Specialization, 2018
The John Hopkins University
BSc in Life Sciences, 2017
National University of Singapore
The tool uses a woman’s own personal information to estimate risk of developing invasive breast cancer over specific periods of time
The main goal of this capstone project is to build a shiny application with the ability to predict the next word.
Due to the onset of greater risks associated with operative intervention in elderly patients, this model aims to shed light on the following questions: What factors can help to predict the post-op outcome in these patients, and help to influence how we manage them surgically. Using XGBoost & RF models, a predicitive model was built over a span of 2 days in the NUHS MIT Healthcare Datathon 2018