Just want to second this comment, their data processes are the key strength of Medallion. Grandparent comment by murbard2 also talks about the importance of this component to quant work (in the last paragraph: "finding new data feeds that provide valuable information")
While Jim Simons is a mathematician and Rentec clearly has hired many brilliant people with PhDs, it's maybe worth mentioning the actual mathematics being used in their work isn't super high level difficult, impossible or secretive. Many of the PhD's working there do not have a PhD in math, but rather something like Physics, so I would say if you are familiar with graduate level math courses you can understand the math needed for this type of work. Math isn't where their edge comes from. Also Medallion is 30 years old, their early work in the mid 80s was done on computers with less processing power than your phone, "Machine Learning" as the term is being used lately or access to supercomputing hardware no one else knows about is also not where their edge came from.
Well said. Where most funds have the same problem 'chollida1 describes here[1], Rentec (and other similar firms) moved past that by establishing the right culture and investing in the right technology from the outset.
They need smart people, but hiring the smartest people and having the most sophisticated models won't do you any good if you can't acquire high signal data, can't clean that data properly and can't rapidly backtest. And if you can't do any of that, adding more data is just going to add more noise.
Given your background, I'd be interested in picking your brain a bit for a few projects I'm working on. If you're looking to remain anonymous would you mind sending me an email (in my profile), or throwing an email up in yours?
While Jim Simons is a mathematician and Rentec clearly has hired many brilliant people with PhDs, it's maybe worth mentioning the actual mathematics being used in their work isn't super high level difficult, impossible or secretive. Many of the PhD's working there do not have a PhD in math, but rather something like Physics, so I would say if you are familiar with graduate level math courses you can understand the math needed for this type of work. Math isn't where their edge comes from. Also Medallion is 30 years old, their early work in the mid 80s was done on computers with less processing power than your phone, "Machine Learning" as the term is being used lately or access to supercomputing hardware no one else knows about is also not where their edge came from.