Data science for operational impact is quite an interesting field of study that has more intersections with social sciences and requires more organizational savvy.

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.