Thread by @SAH16928046

Can data science overcome quantitative finance in terms of employment and salary?

Well, I think you are asking a relevant question.

It can be explained using a Social Darwinian Perspective.

First, the Simple answer =>

YES =>
Quantitative Finance will get absorbed into Data Sciences and Machine Learning Areas as a sub-field.

WHY?

Because many subjects develop independently, but after sometimes they get assimilated into the much larger disciplines or subjects which offers more attractive career remuneration, research opportunities, social status, social mobility and scholarly following and readership.

Statistical models of learning are going through the same phase! They, too, are getting absorbed into DS -Data Sciences and Machine Learning. This is a Darwinian Process as far as I am concerned.
The weak is replaced by the strong.
It is inevitable!

Have a look at some subjects that were taught during the 19th and early parts of the 20th century such as Comparative religious studies, Divinity, Theology, Classics and other liberal arts

which are today no more in demand in the job market, and hence, are not being taught at most of the universities. The faculties have been closed down, and the degrees have been withdrawn.

When Natural Science Courses experienced such difficulty, the professionals from this industry jumped into Econometrics, Corporate Finance, Operations Research, Economics and Quantitative Finance!

No wonder, you find so many mathematicians and statisticians working in the financial services and economic consulting industries.

So, in my opinion, it is the survival of the fittest in the world of academia!

#Darwin

Unfit subjects will die their own death or will ultimately get sucked into much larger and more successful fields. Financial Engineering and QF - Quantitative Finance (largely discredited after the global credit crisis of 2007–09)

would ultimately seek refuge in AI - Artificial Intelligence, Coding, Programming Analytics, Data Mining Methods and the other exciting ML - Machine Learning Models.

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