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Analysts Assemble

Randy Au - Quantitative UX Researcher.

3 min read

Randy Au

This Analysts Assemble interview is with self-confessed data nerd Randy Au, a Quantitative UX Researcher at Google Cloud. Randys career has taken him through several big names in the tech world but it was his article in Towards Data Science, called Succeeding As A Data Scientist At Small Companies / Startups, that got my attention.

As a fellow data generalist, I wanted to dig a little deeper into Randys backstory and see what his path to bringing those general data science skills to Google was like.

Tell us a bit about yourself, how did you get into the data space and what does your data journey look like so far?

Its a weird path. Undergrad majors were Business Administration and Philosophy and I minored in some applied Math. MS was in Communications (the social science) where I did some computational linguistics and learned a bunch about social science methodology and philosophy of science. After that, I did a stint at an office design firm analyzing survey data and automating reports, a very brief stint in an ad-tech firm, then almost 6 years at Meetup the social network (analyst), 2 years at Bitly the link shortener (analyst), 1 year at Primary a kids clothing manufacturer/e-commerce as data engineer, before winding up in Google Cloud as a Quantitative UX Researcher.

About the only common thread through all this was I work as an internal support consultant. I didnt make data products that users directly interacted with, so I was more of a force multiplier essentially. I make teams better =). All through the time, Ive worked with every single team that existed, from engineering and product development to customer service to legal and finance. Math and data and programming is useful everywhere.

These days I use the exact same data science skills I have, and apply it to understanding users as a UXR. I still deal with cross functional teams, tons of separate systems up and down the tech stack, and lots of interesting data questions.

What’s a typical day look like for you in your current data role? (Which tools and languages do you use? Big team/small team/lone wolf? Office-based or remote?)

Typical day is still chaotic as usual =) Theres often 3-5 projects/tasks running in parallel, lots of meetings to help set metrics, get alignment, report results. Theres a team of 7 other qualitative researchers, and Im the only quant in the local group, so its a lot of the building data-driven culture type stuff across a ton of product teams.

Im also in a rather unique position of supporting 8 semi-related products, so a lot of prioritization discussions have to happen.

Day-to-day Ill be using Python and various dialects of SQL, occasionally some spreadsheet magic thrown in.

Youve built up a large following through your blogging. How important do you think it is for data professionals, at all stages of their career, to share publicly what they are doing and learning?

I think the data community in general is very open and receptive, so if someone has something to share, they should just go ahead and do it. Ive personally spent over a decade without really sharing anything outside of twitter comments, but that eventually led to writing articles and people finding those useful (to my surprise).

Where do you see your own data career going next? Building on your technical skills as an Individual Contributor or moving into a more management-based role?

For now my goal is to move somewhat towards management. Theres always bigger problems that need more time and resources to analyze and make happen. Since I cant do it alone I want to do it with the help of others.

If you had a list of “best-kept-secrets” (websites, books, coaches), which would you recommend?

I think the big cluster of people interested in data on Twitter is the best thing ever. Theres always someone around sharing a cool finding, a new tool, or collectively bonding over the insanity that is time zones. Participating in that leads to many good things.

What is the number one piece of advice you give to aspiring data scientists?

Know your data! =D

A close second is respect and leverage domain knowledge experts, which I need to write about eventually.

Where can readers find you online?

Probably easiest on Twitter: