Hui Lin

coffee to code; silence to music

About Me

I am currently a Quantitative UX Researcher at Google. Before Google, I was a statistician for 4 years and a data scientist for 5 years in Iowa. I joined a startup named Netlify in May 2018 and moved from Iowa to the bay area. I have learned a lot from the startup experience.

I love to code in R. Also use Python. My pet peeve is to hear people say R is not as good as Python because it is slower. I am not an Apple fan but do prefer Mac. I am addicted to reading. I love data science. I like drawing, traveling, collecting jokes and anything beautiful. However, my interest in sport is not any higher than fish’s interest in driving Tesla.

I was born in China, moved to the US, and now live in the San Francisco Bay area.

Working and Research Experience


Quantitative UX Researcher

Jun.2020 - present

  • Conduct independent research on multiple aspects of products and experiences.
  • Collect and analyze user behavior through lab studies, field visits, ethnography, surveys, benchmark studies, server logs and online experiments (A/B testing).
  • Work with Designers, Product Managers, Engineers and other UXRs to prioritize research opportunities in a fast-paced, rapidly changing environment.
  • Understand and incorporate complex technical and business requirements into research.
  • Advocate research findings to diverse audiences through written reports and in-person presentations.


Data Science

May.2018 - Jun. 2020

I am building the data science department @Netlify from scratch.

  • Build up and lead a data science team to unlock the optimization for growth and sales enablement (Netlify user has grown 15%+ MoM, revenue has grown 10% MoM)
  • Partner with the infrastructure team to build data pipeline to centralize large scale of user-generated data (Data Lake) in a big data cloud environment using Hadoop and Spark.
  • Design and build Data Mart for business users.
  • Define and lead the development of foundational user behavior analysis, including user conversion model, funnel engagement analysis, marketing attribution, etc.


Data Scientist

May.2013 - Apr.2018

  • Provide data science leadership for a broad range of analytics in North America Marketing
    1. Predictive Analytics
    2. Marketing programs analysis under the observational scenario
    3. Analyze market research survey data using psychometric models and natural language processing; quantitatively study customer perception and identify variables that are predictive of patterns and/or are leading indicators
  • Lead the project of building marketing data pipeline
    1. Build marketing database in the cloud
    2. Automate models and dashboard that track business performance
    3. Social media analytics

Iowa State University

Consultant and Statistician

Sep.2009 - Apr.2013

  • Jun.2012 - Apr.2013, Statistical Consulting for Business College
  • Sep.2009 - Apr.2013, Statistician, Production Animal Disease Risk Assessment Program
  • Aug.2011 - May.2012, Consultant, General University Statistical Consulting and Statistics Consulting for College of Veterinary Medicine


Machine Learning using R

Published in Oct 2017

  • Book Link
  • ISBN 9787121326585
  • Publishing House of Electronics industry

Introduction to Data Science

2019 Dec 31 (Expected)

Book Translation

Applied Predictive Modeling

Published in May 2016

  • Book Link
  • English version by Max Kuhn and Kjell Johnson
  • Translated chapters 1-4, 12-14, 16, 18-20
  • Huazhang Publishing Inc.

R for Marketing Research and Analytics

Published in Oct 2016

  • Book Link
  • English version by Chris Chapman and Elea McDonnell Feit
  • Translated the book
  • Huazhang Publishing Inc.

Statistical Rethinking - A Bayesian Course with Examples in R and Stan

Published in Apr 2019

  • Book Link
  • English version by Richard McElreath
  • Translated the book
  • Huazhang Publishing Inc.