Or you could call me Wilson
(´･ω･`)
I’m currently a data scientist at Google, working on the Pixel product team to turn user behavior into product insights that shape how millions of people interact with their devices. Over the past 8 years, I’ve been deep in the trenches of data science, from building recommendation systems at Academia Sinica to driving product decisions in the mobile gaming industry. My passion lies in transforming messy, complicated datasets into clear, actionable insights that actually matter.
If you’re someone who gets excited about rigorous A/B testing, causal inference, or simply making better decisions through data, you’ve found your tribe. I want to share the methodologies I’ve battle-tested, the statistical pitfalls I’ve navigated, and those productivity hacks that actually move the needle.
Why & What I Write: Personal Learning in Public#
I write primarily for myself. Writing helps me crystallize complex statistical concepts and reflect on the methodologies that actually work in practice. But here’s the thing: if these explorations help me grow as a data scientist, they might just help you too.
Learning Philosophy: I believe in learning out loud. When I tackle a new statistical concept or dive into a complex dataset, I document the journey: the dead ends, the breakthroughs, and everything in between.
Technical Deep Dives: You’ll find rigorous breakdowns of A/B testing frameworks, causal inference methodologies, and statistical concepts that I wish someone had explained clearly when I was starting out. These aren’t surface-level tutorials. They’re the insights I’ve gained from years of making mistakes and learning from them.
Productivity & Tools: As someone obsessed with optimizing workflows, I share the Mac apps, Python libraries, and methodological frameworks that genuinely improve how I work. No fluff, just tools and techniques that have passed the real-world stress test.
Every post is grounded in real experience: the kind of hard-won insights you only get from years of working with data in high-stakes environments.
Get in Touch#
Whether you want to discuss experimental design, debate statistical methodologies, or share your own data science journey, I’d love to hear from you.
- Email: haosquare.tw@gmail.com
- LinkedIn: Connect with me professionally
- Github: Explore my code and statistical implementations, where theory meets practice in Python and R