Tag: Qualitative UX Research

Data Science Storytelling: Quantitative UX Research in Google Cloud with Randy Au (Part 2 of 2)

In this second part of my interview with Randy Au, he discusses the techniques he used to teach himself to code and his approach to programming and data science as a social scientist.

Here is Part 1 of our interview.

Prior to joining Google, he spent a decade as a mixture of a data analyst, data scientist, and data engineer at various startups in New York City and before that, studied Communications. In his newsletter, he discusses data science topics like data collection and data quality from a social science perspective. Outside of work he often engages in far too many hobbies, taken to absurd lengths.

Click here to learn more about the Interview Series this is a part of.

More about Randy:

Data Science Storytelling: Quantitative UX Research in Google Cloud with Randy Au (Part 1 of 2)

Randy Au, a Quantitative UX Researcher at Google, explains how he leverages his backgrounds in communication, statistics, and programming as a quantitative UX researcher in Google Cloud to analyze and improve Cloud Storage products.

Here is Part 2 of our interview.

Prior to joining Google, he spent a decade as a mixture of a data analyst, data scientist, and data engineer at various startups in New York City and before that, studied Communications. In his newsletter, he discusses data science topics like data collection and data quality from a social science perspective. Outside of work he often engages in far too many hobbies, taken to an absurd lengths.

Click here to learn more about the Interview Series.

More about Randy:

Designing Machine Learning Products Anthropologically: Building Relatable Machine Learning

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How do we build relatable machine learning models that regular people can understand? This is a presentation about how design principles apply to the development of machine learning systems. Too often in data science, machine learning software is not built with regular people who will interact with it in mind.

I argue that in order to make machine learning software relatable, we need to use design thinking to intentionally build in mechanisms for users to form their own mental models of how the machine learning software works. Failing to include theses helps cultivate the common sense that machine learning is a black box for users.

I gave three different versions of this talk at Quant UX Con on June 8th, 2022, the Royal Institute of Anthropology’s annual conference on June 10th, 2022, and Google’s AI + Design Tooling Research Symposium on August 5th, 2022.

I hope you find it interesting and feel free to share any thoughts you might have.

Thank you for the conference and talk organizers for making this happen, and I appreciate all the insightful conversations I had about the role of design thinking in building relatable machine learning.