I'm the guy who talked about the Brioni suits. I don't want to get into an argument about you underestimating my abilities and job. I would like to chime in based on what I know to 1) show you that I really know what I'm talking about 2) add value to the conversation.

I've been in quantitative trading for five years to know enough about the advancement of automation / computerization in financial markets. Sure, the current wave is the replacement of humans with machines. For the prospective candidate wanting to ride this wave, he needs to also recognize the barriers of entry for someone new in the scene. The candidate here is as mentioned - a guy in banking transitioning to a quantitative role via a rebranding of himself with a masters in computer science from Harvard / MIT. It's one thing to jump from banking to quantitative role, it's another to be profitable in the quantitative type industries.

Thus, my opinion on the two ways on how to be ahead of the curve given the automation that is happening in most markets:

1) Start ups / SMEs

I believe any start ups could use an automation in their processes. Some of the work here are intellectual, namely using A.I. to predict most travelled routes for Uber drivers or statistics to forecast supply of groceries. I also believe a Masters from Harvard will equip you enough to add value in what they deem as a quantitative task.

Further, by design, start ups are piercing a new area. So the success of the start hinges less on the standard of automation (contrast this with trading below) and more on the idea of the start up. Thus, I see the possibly of the founder taking the capital risk of his idea but paying you $10k a month because he knows you can deliver a top product in your area of expertise.

Also by design, the high degree of failure of start ups in general shifts the expected success to the downside. You will have to weigh how probable the start up will succeed with the payoffs.

2) Automated trading

This area needs to be proceeded with most caution as one should have some perspective on what it takes to be profitable here.

Those who don't know the depths of automated trading are naive to think that as long as one automates a certain trading process, one can immediately beat out the human traders. First, a majority of hedge funds that used to trade purely discretionary are making in roads to automate their execution processes. I know of two big funds in Singapore that I shall not name that are already doing this. Simply put, the trading idea is discretionary but execution is automation. This means that there are much less opportunities for someone who thinks they can just come in a fund and fill in the gap by automating a process. The point is most processes are ALREADY automated.

Second, then comes the degree of how much automation can be done. If you are in the industry long enough, you are humbled at where you stand in the spectrum of standard of work. In short, Singapore companies are VERY far behind the curve compared to US companies. Think of the spectrum as the time required to execute a trade. Where a fully automated trading company in Singapore takes 500ms, a US company could very well take 50ms. Two reasons are:

a) Time and level of research done: Those companies who are already in the quantitative trading scene for 10 years have done the PhD level research to know how to optimize their algorithms.

b) Capital: US firms can sink in millions of dollars to buy microwave towers to transmit their messages vs Singapore firms who think colocation is new.

The point is that even if you enter quantitative trading with a Masters from Harvard, you'll always be 5 years behind the companies that have been doing it for that amount of time. Think of the DE Shaw, Two Sigma and Citadels of the world.

Scope of the market comes into play. If you're trading in a global market like forex, your profits will definitely be eaten by these big companies. But if you're trading in Singapore equities, then it's possible that the automated part of the trading process could yield extra PnL.

That's why I've been observing the scene and noticed that the medium size funds in Singapore who though they could benefit from automated trading end up failing.

Basically, getting a Masters from a top school doesn't automatically mean you've join the wave of automated trading. You'll need to know who are the right employers that are at the appropriate stage of the wave and are able to constantly stay in front of the pack.

You don't want to be in a position to think that you're doing something new when some guy has done it 2 years ago. And when you push it into the market, it fails because you're already behind. Buzz words like "machine learning", "dynamic programming", "markov models" sound super to a new computer science graduate. It takes maturity to see out of all these technologies, which have not yet been implemented and when implemented puts you ahead of your competition.

Before, I use to jump to any quantitative opportunity in any decent firm. Now, not so. If your trades are always 200ms faster than non-automated firms but 100ms slower than automated firms, you're not gonna survive.

TLDR: Automation has been maturing and a Masters from Harvard doesn't put you ahead of the curve, at best, I say at the 40th percentile. Or if you're doing fine in banking, it is probably better to stay there and not be lured into what you think is still a lucrative field.

At the very least, I hope I have proven that I should be given a chance to show that I know what I'm talking about.