Leonard Lee

Data Scientist, Front end Enthusiast

Turning Data into Decisions, Code into Impactful Experiences

About

I’m a data scientist and front-end enthusiast with a passion for building thoughtful, data-driven digital experiences. With experience at Shopee and currently at Singapore’s Ministry of Defence (MINDEF), I’ve worked on systems that balance performance, security, and usability at scale.

My work bridges the gap between data and design—whether it's uncovering insights through machine learning models or crafting interfaces that make complex information more intuitive. I believe great products are built when analytical precision meets user-first thinking.

At Shopee, I contributed to data-powered features in a fast-paced e-commerce environment. At MINDEF, I focus on developing secure, reliable systems with meaningful impact. My technical toolkit spans Python, SQL, React, and modern data science frameworks.

Outside of work, I’m usually out for a run, discovering new music, or tinkering with side projects that blend code, creativity, and curiosity.

Experience

  1. 2023 — Present

    Data Scientist · MINDEF

    Driving data science projects at MINDEF/SAF, I enhance decision-making in policy, operations, and resource management. I manage all project phases, ensuring quality and technical precision, while collaborating with stakeholders to deliver actionable insights and communicate complex findings to both technical teams and senior decision-makers.

    • Python
    • Javascript
    • React
  2. 2019 — 2022

    Data Scientist · Shopee

    Leveraging expertise in data science and engineering, I develop cutting-edge machine translation models at Shopee, optimizing AI-driven solutions to improve multilingual communication. By analyzing large datasets, I enhance translation accuracy, facilitating seamless cross-border commerce. My work helps deliver an exceptional user experience, driving growth and engagement in Shopee’s global marketplace.

    • Python
    • CUDA C++
  3. May — August 2018

    Data Science Intern · DSO

    Introduced and implemented a deep learning technique, the Deep Markov Model, to predict soldiers' internal body temperatures and identify those at high risk of heat-related injuries in real-time. Using PyTorch, I experimented with model variations to optimize performance with minimal supervision.

    • Python
    • PyTorch
    • Pyro (Deep Markov Model)

Projects

  1. Pomodoro project image

    Simple Pomodoro App

    A personalized pomodoro web application that can ensure time management for your daily tasks.

    • HTML
    • SASS/CSS
    • Javascript
    • Node.js
    • Express
    • MongoDB
  2. Portfolio website image

    Portfolio Website

    My first portfolio website built with SASS

    • HTML
    • SASS/CSS
    • Javascript
    • Github Pages

Education

  1. 2019 — 2021

    National University Singapore (NUS) · Graduate

    Masters Degree in Computer Science

    • Natural Language Processing
    • Bridging System and Deep Learning
    • Trustworthy Machine Learning
    • Knowledge Discovery and Data Mining
  2. 2015 — 2019

    National University Singapore (NUS) · Bachelors

    Bachelor of Science (B.Sc) (Hons), Applied Mathematics with specialization in Mathematical Modelling and Data Analysis, Second Major in Statistics

    • Applied Mathematics
    • Statistics
    • Data Science
    • Python
  3. 2011 — 2012

    Anglo-Chinese Junior College (ACJC) · GCE A-Level

    Pre-university Student

    • H2 Physics
    • H2 Chemistry
    • H2 Mathematics
    • H1 Economics

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