> people with both a fairly strong quantitative background and a strong software engineering background. In my experience this intersection is a null set. And

> people with both a fairly strong quantitative background and a strong software engineering background. In my experience this intersection is a null set. And not just that it's an extremely rare feat to pull off IMO, the mental bandwidth and time needed to be good at one of those two alone would consume one person fully. This is why quant/stat specialists were paired with ETL/data-pipeline specialists to build end to end solution. One reason Data Science became such a hot role back in the day was that it was amorphously defined; because no one knew what exactly it entailed folks across a broad range of skill sets (stats, data engineers, NoSQL folks, visualisation and so on) jumped into the fray. But now companies have burnt their hands, they have learnt to call out exactly what's needed; even when they advertise for DS role they specify what's required of them. For example, this page on Coursera[1] is clear about emphasis on Quant, which is a welcome development IMO. [1] https://www.coursera.org/articles/what-is-a-data-scientist https://www.coursera.org/articles/what-is-a-data-scientist