<div class='resize'><iframe src="https://docs.google.com/presentation/d/e/2PACX-1vTXoMs7M-lpmnyV7dLb4wga9wvyPbkz3TwT-dBkc7gEg0Jb0_HSWX_xYkB8rLBEe8Msf5HifKA13zJb/embed?start=false&loop=true&delayms=3000" frameborder="0" width="960" height="569" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe></div> Much of the data science discourse at the time was geared towards what I call [[Product Data Science]], where the goal was to build highly scalable machine learning systems that solve a general problem (think Uber's surge algorithm). However, I think an equally interesting area of data science is what I'd call [[Operational Data Science]], where there is significantly more iteration, working with domain experts (local marketers, country managers), to solve problems with "human-in-the-loop". Thinking about data science this way has enabled me to overcome many adoption barriers in my past work. I presented this at [[SGInnovate]], a data science incubator bootcamp in [[Singapore]], while I was still at [[Uber]]. ## See also - [[A data team's product is decisions]] - [[Dealing with model uncertainty in data products]]