About Me

I'm Nicole (she/her/hers), a Machine Learner leader in Chicago. Diversity, Equity, and Inclusion are very important to me, and I'm always happy to chat about how tech companies can improve in those areas. As my teammates know, I am extremely into organizing things like code (every single person has had a code review from me asking them to alphabetize their imports). I also enjoy thinking about how to make DS processes more efficient.

Currently, I'm a Machine Learning Engineering Manager at Spotify. My team works on improving candidate pools for recommendations.

I have a PhD in Physics from Berkeley; my research focused on computational neuroscience. After grad school, I spent time at the Exploratorium and doing QA/Tech Support at Automatic.

I then moved to Chicago and worked at the Center for Elementary Math and Science Education at the University of Chicago. I developed online interactive math activities and worked on a data analysis tool.

I started my Data Science career at Shiftgig. I spent all day playing with fun tools like Jupyter notebooks, pandas, scikitlearn, and PyMC3.

After Shiftgig, I spent almost four years at ShopRunner. I started as an individual contributor and left as the Director of Data Science. We worked to help ShopRunner improve personalization and automation at scale.

You can often find me at PyLadies and PyData Chicago meetups.

In January 2019, I spent a week at the Recurse Center.