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Julie Zhou
Experienced analytics leader with a strong background in marketing and finance.
Julie Zhou
Julie Zhou is a seasoned analytics professional with experience in consulting, finance, and accounting. She currently works as the B2B Marketing Analytics Lead at DoorDash, a food delivery service, where she has been since January 2022.1
Prior to DoorDash, Zhou held several analytics leadership roles:
- Director of Analytics at The Athletic, a sports media company, from February 2020 to December 20211
- Senior Analytics Manager at Lumos Labs (Lumosity), a cognitive training company, from February 2015 to February 20201
- M&A Advisory Manager at PwC, a professional services firm, from July 2006 to August 20141
Zhou has a strong educational background, holding an MS in Accounting and Information Management from The University of Texas at Dallas and a BBA in Finance and BA in International Studies from Southern Methodist University.1
She is also a Certified Public Accountant (CPA), Certified Fraud Examiner (CFE), and has passed the Japanese-Language Proficiency Test N2.1 Additionally, Zhou is a certified scuba diver and has traveled to 13 countries across 3 continents.1
Highlights
Taste is invisible until you try to write it down.
This is probably my biggest lesson with AI building as of late.
At @TeamSundial, I get to work with really friggin' amazing analysts who know the art, and I see how much of our collective time now is now spent turning that art into playbooks or skills for an LLM.
Encoding things like: "How would a great analyst actually look at this metric move?" or "What is ACTUALLY the interesting signal in this story versus noise?" or "How can we know if a product change actually moved the needle?"
It's really humbling work!
You write an instruction set. The LLM misses. You add more context. It still misses. You add even more. Now it's confused. You strip it back. Now it's too vague. You try a different framing. Better, but inconsistent. Works on Monday, fails on Tuesday. You go again.
I've come to realize the gap between 70% quality and 95% quality is not 3 or 4 big things. It's more like 100s of small things. Which is exactly why you can't write an article about it, or copy it, or shortcut it!
This gap is taste, quantified. The accumulated weight of a thousand small judgments you don't notice you're making, until you sit down to externalize them and realize you can't.
Being good at something is not the same as being able to articulate why you're good at it.
I now see two bottlenecks to making something better than today's generic AI:
- Can you see what better looks like in the first place?
- Even if you can see, can you articulate what that is in a way that the LLM can understand and systemize?
#2 is now a new craft, the art of distilling the art.
The people who can do it well are the ones building standout products.
Some people like restoring old cars.
I like restoring old AIM convos from 2002 whose logs I have saved meticulously across a dozen different computer moves ^_^ https://t.co/XgNmmUxLYR



