5 Questions with a Woman in Data Science: Michelle Carney
By: Berkeley I School
Tags: Machine Learning, Data Science, Ischoolalumni, Ischooldiversity, Women In Data Science

UC Berkeley I School is proud to support women in data science. This series highlights alumnae and students at the school working in data science.

Michelle Carney is a graduate of the Master of Information and Management Systems (MIMS) class of 2018. Michelle is currently a Machine Learning Specialist and UX Designer at Amazon; she’s the founder of the Machine Learning and User Experience (MLUX) Meetup; and she’s a machine learning instructor at the Stanford d.school.

Michelle

What influenced you to pursue a career in data science?

I started doing data science during my undergrad at Cal in neuroscience, and it was really fun to see how you can model behaviors and the brain with math and equations! I ended up going into UX because of my research background, and now I use the same type of data science techniques from neuroscience to help understand how customers use products and how to improve their experience.

Can you tell me about the work you are doing now?

Currently, I work at Amazon Music, and I am on the User Experience (UX) team. I combine mixed-methods research (qualitative and quantitative) to understand how to improve customer experience and create new experiences for them. I am constantly shifting between data and qualitative research — what are some of the things we see in qualitative research and can we see these trends at scale in the data? Are there any insights from the data that can help us understand and develop a qualitative study? It’s a lot of python, data visualization, and user interviews!

At the I School I was able to take data science and machine learning classes and apply them to real people.

How did what you learned at the School of Information develop your skills and further your career goals?

At the I School I was able to take data science and machine learning classes and apply them to real people: if we have a Cox proportional hazard model that shows patient drop off, what does that mean about a possible intervention we can design to help increase retention in an app or doctor’s office? I also was able to explore how to do UX research on data science applications and machine learning, when your models aren’t there yet. The most important classes I took weren’t actually Data Science or UX classes though, they were: Info Visualization with Marti Hearst (to help translate the insights from data in a meaningful way) and Technology and Delegation with Deirdre Mulligan (to understand the entire ecosystem of tech and that artifacts we design have politics).

Michelle (right) speaking on a panel at the WiDS (Women in Data Science) Berkeley 2019 Conference

Who inspired you when you were young?

My mom! She’s awesome and very smart and talented and has had careers in science and law and has shown me that life is a journey and everything you try can help inspire you and teach our valuable lessons. Also, Marie Curie — she’s just amazing and one of the first people to win two Nobel prizes in two different fields even with all the adversity she was facing!

What advice do you have for young women who want to work in the field of data science?

It’s never too late to start data science! I view data science as a skill-set or technique, but even in data science there are a ton of different fields and you’ll probably want to specialize (computer vision, NLP, healthcare, etc). If you’re coming from another background (medicine, economics, literature), that makes you an even stronger candidate — you have domain expertise in a different area and have probably already been doing the basics of data science (aka hypothesis testing, validation, etc). You just need to learn the techniques! Also, if you’re a student at Berkeley, check out the free resources at the d-lab, they’re great!

Watch Michelle’s Women in Machine Learning panel discussion online.