Ryan Cybulski
About Ryan Cybulski
Ryan Cybulski is a Staff Data Scientist at Kohl's with extensive experience in data science and astronomy.
Title
Ryan Cybulski currently holds the position of Staff Data Scientist at Kohl's. He has been in this role since 2022. This title signifies his advanced standing and expertise within the field of data science.
Professional Experience
Ryan Cybulski's employment history encompasses significant roles in both the retail and healthcare industries. From 2019 to 2022, he served as Sr. Manager, Data Science at CVS Health in Woonsocket, Rhode Island. Prior to that, he held the position of Advisor, Data Science at the same company from 2017 to 2019. Ryan was also an Insight Data Science Fellow at Insight Data Science for 3 months in 2017, located in the Greater Boston Area. Additionally, he worked as a Postdoctoral Research Associate at Tufts University from 2016 to 2017 and as a Predoctoral Fellow at Harvard-Smithsonian Center for Astrophysics from 2012 to 2013.
Educational Background
Ryan Cybulski achieved a Doctor of Philosophy (Ph.D.) in Astronomy from the University of Massachusetts Amherst, where he studied from 2008 to 2016. He earned his Bachelor's Degree in Astronomy and Astrophysics from Penn State University, completing the program from 2003 to 2007.
Data Science Expertise
Ryan Cybulski has applied advanced data science techniques throughout his career. He has employed machine learning to optimize inventory allocation and predict mechanical failures in refrigerators. Additionally, his proficiency in time-series forecasting methods such as ARIMA and FB Prophet has supported supply chain and inventory management initiatives within retail businesses. His work demonstrates expertise in clustering analysis and graph theory techniques like minimal spanning tree and Voronoi tessellation, particularly in the realm of astronomical research.
Research in Astronomy
In his astronomical research, Ryan Cybulski has merged large and disparate datasets with multi-wavelength observations to investigate galaxy evolution. His work has focused on understanding the environmental factors influencing galaxy environments. Techniques such as clustering analysis and graph theory, including minimal spanning tree and Voronoi tessellation, have been pivotal in his studies of galaxy environments.