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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]]