Core quants and desk quants on main street

The more perceptive of you might have realised that I’m in the job market.

Over the last one month, my search has mostly be “breadth first” (lots of exploratory conversations with lots of companies), and I’m only now starting to “go deep” into some of them. As part of this process, I need to send out a pitch to a company I’ve been in conversation with regarding what I can do for them.

So I’ve been thinking of how to craft my mandate while keeping in mind that they have an existing data science team. And while I was thinking about this problem, I realised that I can model it like how investment banks (at least one that I worked for) do – in terms of “core quants” and “desk quants”.

I have written about this on my blog before – most “data scientists” in industry are equivalent to what investment banks call “core quants”. They are usually highly technically accomplished people; in many cases they are people who were on an academic path that they left to turn to industry. They do very well in “researchy” environments.

They’re great at running long-gestation-period assignments, working on well defined technical problems and expressing their ideas in code. In general, though (I know I’m massively generalising), they are not particularly close to the business and struggle to deal with the ambiguities that business throws at them from time to time.

What I had mentioned in my earlier post is that “main street” (the American word for “general industry”) lacks “desk quants”. In investment banks, desk quants are attached to trading desks and work significantly closer to the business. They may work less on firmwide or long term strategic projects, but their strength is in blending the models and the markets, and building and making simple tweaks to models so that they remain relevant to the business.

And this is the sort of role in which I’m planning to pitch myself – to all potential employers. That while I’m rather comfortable technically, and all sorts of different modelling techniques, I’m not “deep into tech” and like to work close to the markets. I realise that this analogy will be lost on most people, so I need to figure out a better way of marketing myself. Any ideas will be appreciated.

Over the last month or so I’ve been fairly liberal and using my network to get introductions and references. The one thing I’ve struggled with there is how they describe me as. Most people end up describing me as a “data scientist”, and I’m not sure that’s an accurate description of what I do. Then again, it’s my responsibility to help them figure out how best to describe me. And that’s another thing I’m struggling in. “Desk quant” doesn’t translate well.