AB Testing of Data Science Solutions by Reenah Nahum Muldavski Visit https://rstats.ai/nyr/ to learn more. Abstract: In recent years data science models have increased the efficiency and value of products across many industries. Data science models in many cases affect the product backend logic in a fundamental manner (such as directing customers in queues), and classic "split testing" is hard or even impossible to implement. AB testing methodology for frontend features is solid and well defined, however, the methodology for testing the value of backend enhancements is not as firm and well applied. In this talk we will cover experimental design best practices for testing and measuring the value of data science models. Presented at the 2021 New York R Conference (September 9, 2021)