Interview with Lisa-Christina Winter, Senior Product Researcher, Chatroulette

Lisa-Christina Winter is a data scientist, speaker, and AI article author. She has recently been nominated as one of the AI Time Journal inspiring  data scientists to follow in 2020 .
We thank Lisa-Christina for sharing several insights in this interview, including her reflections on how data scientists come in 'all different shapes and colours', the importance of a fundamental understanding of the business goals, and valuable resources that helped her along the way.
This interview is part of the  Data Science Interview Series 2020 .

How did you first get into data science?
I fell in love with statistics when I studied psychology (as a 'science') in Austria. Originally, I didn't expect the amount of mathematics and statistics I would come across. I was exposed to Kolmogorov and Smirnov more than to Freund and Jung, and honestly, I was terrified. Because I had to spend a significant amount of my time as a student trying to develop a mathematical mindset, I realised that this is something I actually love. I would never have found out any other way. My statistics fear turned out to be my biggest (professional) blessing in disguise. So yeah, I fell in love. And that's where my own personal statistical love story (admittedly with a heavy pinch of statistical Stockholm syndrome) started. After that it came just naturally to me to acquire the technical skills I needed to be able to work with large data sets. I spent most of my professional years in data science consulting before I entered the world of product research. Today, I mainly work with R and SPSS, so I'm clearly more on the statistics than on the programming side of data science.
How is data science used to create value in your current project(s)?
Data science is an important aspect of pretty much all of our projects. At Chatroulette, for instance, we use machine learning algorithms to predict the likelihood of a person showing inappropriate behaviour on our site. We have brilliant machine learning engineers at Chatroulette. In my own projects at Chatroulette, I use large amounts of data for experimental purposes on a daily base.
What are the key skills that you use every day as a data scientist, and how did you develop them?
Statistics, experimental design and creativity. Admittedly, I've been lucky enough to work in amazing teams including highly supportive (data and software) engineers, which made it easy for me to really be able to focus on the analytical side of things.
What are the top challenges you currently face as a professional data scientist, and how do you go about tackling them?
I think the biggest challenge is still to accept that data scientists come in 'all different shapes and colours', if you will. I'm not a hardcore programmer and I will never be one. And that's not something I ever even considered. However, I consider myself a proficient experimental statistician with a background in...