It's not new that Wall Street is hiring Ph.D scientists and mathematicians (that's been happening since the 80s) but what's changing is the kind of role they are getting hired for.
From the 1980s up until 2005 or perhaps even later, most Ph.Ds were hired in "quant" roles, that is, to build mathematical models that could price and manage the risk of derivatives. They were generally not in trading roles.
More recently (i.e. in the last decade) it's common to hire Ph.Ds as traders - that is, to write algorithms that are used to make trading decisions. This requires a rather different set of skills - instead of being skilled in stochastic calculus, partial differential equations and numerical methods, the quant trader is skilled in statistics, data analysis, optimisation and machine learning.
It's true that Wall Street has Ph.Ds hired to build trading models for many years (e.g. Renaissance) but the change is that this is no longer an esoteric fringe pursuit. It is seen as standard that trading desks at investment banks will have a large number of quant traders. Large European investment banks have heads of trading who are quants. Quantitative hedge funds are no longer mysterious and exciting - many of them use well understood models that have been widely replicated across the industry.
To be honest, I am surprised that it has taken this long. Finance is so obviously suited to these kinds of quantitative methods that only an aggressive rearguard action by voice traders has been able to keep them at bay. The question is no longer whether quantitative traders and their algorithms will largely replace voice traders, but how long the voice traders will manage to hold out.
Jane Street is an interesting special case. From my (somewhat limited) interactions with them, they are neither wholly voice traders nor wholly algorithmic. Instead, their researchers and traders build algorithmic trading systems which can be "driven "by humans. For example, the system will continually calculate a set of useful metrics that a human trader uses to make a final buy/sell decision, and trade is then immediately executed by high frequency execution algorithms. Or the human trader decides to make a complicated spread bet (e.g. long an ETF vs. a cost-optimized basket of the underlying stocks) and the algorithm goes out to execute the basket as effectively as possible.
I think there's an interesting parallel to "Centaur chess" [0] where the combination of a computer and a human is much more powerful than either of them acting alone.