I had always presumed the vast majority of economists working in tech were simply doing transfer pricing... shuffling profits overseas to minimize taxes.
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Some, yes. However, a lot of economist work goes into real-time ad auction platforms, marketing, fraud detection, and many other places where human interaction and incentives meet automated systems.
As an economist in the technology/data science space, I've found that the training and methodology brings a lot to table, such as the feature engineering side of ML or investigating business processes for incentive misalignment.
I studied econ undergrad but don't see much reason to get a PhD outside of academia or leveraging a public sector job such as a Fed job into a private sector finance role. I can see some of the technical skills such as survey construction/analysis, bayesian analysis/higher level statistics being somewhat useful, but a lot of that is also very role specific and can be learned on the job, no? Thinking with a game-theory/incentive-centered mindset is something that does not require too much formal econ knowledge.
Econ's undergrad degree is remarkably unrepresentative of Econ grad school. But every phd program in any subject is training you to do research first and foremost, so don't do it for technical training.
Can you speak to why the gap is so large between undergrad and grad econ programs?
The level of math. An Econ/math double major is about the right background for an Econ phd program. (I was a math major in undergrad and didn't feel like a lack of much exposure to Econ hurt me much in grad school, but it would have given me better perspective on the field.) Many Econ undergrads want a "businessey" degree and don't want to take math beyond calculus, so trying to teach "real" macro (which would require coursework equivalent to differential equations) or "real" micro (similar to but less fussy than real analysis) or even empirical research (statistical tools that build on regression) would be rough going. So we teach "intuition" and watered down versions of the results and punt on justification. Plus the "core" undergrad courses have a large service component --- we need good business students to be able to take intermediate micro, for example, so unilaterally ramping up the required smath wouldn't be feasible even if some of the students wanted it.
tl;dr phd programs can spend a lot of time teaching students how to use and extend mathematical tools and can cherry pick students with the right backgrounds. Undergraduate programs have a big service component, have to assume a range of mathematical preparation, and have to actually teach "economics" in their core courses -- game theory needs to teach game theory, not how to be a game theorist.
That sounds like accounting, not economics.
Isn't econ one of the most mathematical of the social sciences?
Apparently there's a lot of mathematical overlap between grad school econ and fields in Big Data: computer vision, machine learning, etc.
Econometrics is essentially a field of applied math. Statistics is also pretty closely related to econ, though it sounds like theres a wide range of statistics backgrounds between programs. I had a roommate that studied econ/statistics and always thought it was pretty neat some modern algorithms didn't come from computer scientists, but from statisticians (particularly Random Forests).