About this blog
Most statistics education stops at textbooks. This blog bridges the gap — translating causal inference, A/B testing methodology, and econometric theory into the kind of reasoning that holds up when a PM asks "is this experiment actually working?"
Who writes this
I'm a Senior Data Analyst at a e-commerce unicorn, working on experimentation, recommendation, and search analytics — close enough to product and engineering that the role often looks more like Product Data Scientist in practice. Before that, I finished a PhD in Economics, where I spent most of my time thinking about causal identification — instruments, difference-in-differences, and whether any of it would survive contact with real data.
The short answer: some of it does. A lot of it needs translation. That translation is what this blog is about.
Academia
PhD, Economics
Applied Microeconomics — causal inference, IV, DiD. Job market paper on social network formation.
Research
Research Institute
Institute for International Economic Policy. Applied econometrics on government policy.
Startup
Early-stage Startup
Built Bayesian A/B frameworks, surrogate index LTV models, and zero-inflated payment models from scratch.
Now
E-commerce Unicorn
Senior Data Analyst working like a Product Data Scientist — sequential testing, ad attribution, and recommendation systems at scale.
What you'll find here
The posts here are technical but not academic. Every method gets a "so what does this mean for the experiment I'm running next week?" treatment. I write mostly about things I've had to actually implement — and the parts that textbooks skip over.