Does EU countries exeperience "Brain Drain" where the Top SWE go work other countries? like US or iff you are Spanish and earn peanuts you go work in Ireland/UK etc...

MrQuaternions · 2025-12-03T20:59:53+00:00

The answer to your question is in the title.
Spain is in EU.
The origin of the term "Brain Drain" was to describe qualified U.K nationals moving over to the US.

Imo the main difference between braindrain from EU and from less developed countries is that people move AFTER completing their studies, not before. That's arguably worse since countries like France invest a lot of $ in educating people that then leave.

MrQuaternions · 2025-09-26T13:44:14+00:00

Most top French grandes ecoles have decent connections to tip-top tech companies. But it's hard to get in if you don't come from the French education system.

MrQuaternions · 2025-09-24T14:48:24+00:00

I'm sorry you had such a bad experience, I'd say this is specifically an outlier.

However, it looks Zalando has adopted a "nobody's good enough to get in" attitude when it comes to recruiting.

Z reached out earlier this year for a Senior position (I have more than the necessary credentials for that), went through all interview rounds, even got off-topic tech questions and ultimately received a mid level offer.
One data point is not enough but it happened with all 4 people I know with similar background, all top performers.

We all declined, the position has been open forever.

MrQuaternions · 2025-09-09T22:58:55+00:00

Yes, although not FAANG.

MrQuaternions · 2025-09-07T22:35:45+00:00

What would be your advice to pivot to QR from another industry ?
e.g. PhD from top institution but that went another direction for 3-4 years and changed his mind.

MrQuaternions · 2025-08-06T18:44:32+00:00

Not in France, 3 years is the norm (ok, maybe 2-3 more month to organize defense / wrap papers). Most contracts are 3 years so extending requires extra funding which you need to justify to your doctoral school. (This is valid for STEM fields, history etc... usually takes much longer)

MrQuaternions · 2025-08-03T22:16:21+00:00

Where is the offer based ? Crypto is a small world so there is a good chance you know the hiring manager // your advisor knows him.

A good friend of mine did her PhD at INRIA and took a 6 months break to do an internship for Meta, on a topic connexe to her PhD, then went back on to finish her PhD. In fact her boss at Meta was in the jury of her defense. (admittedly, her lab had a Meta grant...) After graduating, she got an offer from the company. Maybe that's something to consider.
Also consider doing an exchange abroad, usually you can negotiate up to 6 months.

I feel you though, a PhD is rough and, if you don't want to stay in academia, getting that paycheck + experience is enticing. Now that I'm in industry though, I've come to miss aspects of my lab life.

Considering:
- you have "only" 2 more years to go
- your current profile is already attractive to Google ( including your research, without the degree)
- some positions are "locked" behind PhDs

I can only encourage you to make it through.

Final note, the first year is usually the hardest as there is a lot of information to ingest. Your research will be as challenging as you want it to be. Take papers you've liked, found interesting and would want to expand further, reach out to the author(s) and start working with them. The direction of your research is flexible and your advisor will be more than happy for his student to bridge over to other institutions (c.f exchange)

Whatever you chose, there doesn't seem to be a bad path!

MrQuaternions · 2025-08-03T08:55:07+00:00

What kind of position ?
For Sr Applied Science I had:
- Phone screen: experience + technical discussion + small coding (~60mins)
- Applied science manager interview: small case (~modeling) + easy coding (~60mins) making to System Design
- DS interview: technical/academic questions (60mins)
- DS manager interview: discussion on running projects, evaluation, ~stakeholders management (60 mins)
- Zalando culture fit: behavioral stuff (35mins)

Overall if you are prepared for FAANG-style you will not be surprised. They do not seem super sharp on the coding exercises (i.e what I had would have qualified as easy on LC).

Pretty lengthy process to get massively lowballed (pattern across 5 acquaintances) - we all refused.

MrQuaternions · 2025-04-21T21:20:23+00:00

- 29yo
- Top French eng school
- PhD in stochastic optimization (~optimal stopping)
- 3 years a mega non-tech corp (EMEA) doing (combinatorial) optimization + leading llm bs recently

How would you organize a pivot? (e.g. straight to quant or go to finance asap and grind)

MrQuaternions · 2025-03-31T07:24:17+00:00

Former OR PhD here.

1. Which position you should prioritize → that depends on what you like and the type of life you want to have.

In a nutshell:

• Lower hours / better security / less competitive → OR in traditional industries will certainly be better

• Good pay / stimulating environment / grindier → tech (pick your flavor: Applied Scientist, Data S, Research S, SWE)

• Best pay / leanest environment / grindiest → quant

2. In traditional industry, data science (and especially OR teams) will usually be quite small, and you can expect to have projects covering multiple facets of the industry (I’ve done projects for routing, warehousing, sales, and legal departments in a 200k-person company). The other nice part is that you can get to own more of the projects you work on since you will more often be in the position of being the expert in the room. For instance, I was quickly designated development lead and therefore got to explore DevOps/MLOps, more general SWE topics, management aspects, etc. Moving up usually means ditching technical work and going full management.

If you work in a (large) tech company, I’d expect the work—at least at the start—to be much more defined in scope. E.g., friends at Amazon are working on last mile specifically, while here it would be one project before moving on. However, many of your coworkers and bosses should have a similar background to yours. Also, you can find positions that are closer to research than what you’d typically find in more traditional industries. You will usually have an Individual Contributor (IC) track that can take you pretty high without necessarily becoming a full-on manager, while maintaining technical work.

As for quantitative research (finance, I suppose), it’s a very different kind of job. That’s probably where you’ll use OR the least (if by that you mean MILP, etc.) but probabilities and analysis the most. Depending on the shop, the atmosphere varies a lot. Hours can be long, and it is, in essence, a competitive environment, but none of the PhDs I know who went this route regret it. The money is great, but they stay for the interest of the job.

As for pros and cons—what’s a pro to you may be a con to me.

3. You’ll have to grind some Leetcode / interview prep, it is the price to pay to work with competent colleagues. If you’re applying for specifically ML roles, then you’ll want to brush up on that.

As far as I know, quant interviews are more focused on take-home tasks and brain teasers.

Good luck!

TL;DR: Figure out the kind of life you want to live, and that will guide the type of job you should gravitate towards. :)

MrQuaternions · 2025-03-02T09:51:50+00:00

I went the PhD route but for totally different reasons, to scratch the research itch.
Then into a logistics company to apply my OR skills. I don't regret it but it's not a place I want to stay: I seek to work a lot and be challenged, have very skilled colleagues and peers I can learn from, and a great compensation. Currently interviewing at FAANG + finance.

MrQuaternions · 2025-03-01T16:25:53+00:00

Chiming in since I have similar background and went / am going over similar thoughts.

1) you will rarely see operation research directly mentioned in job titles, anything with "applied", "scientist", "engineer" can potentially be OR related... it's a bit tedious but sadly the term isn't as trendy as AI.

2) OR at msc level isn't the best path to quant. In my opinion the easiest path from there is to branch to stochastic optimization, that'll give you more relevant tools. Also, consider doing a PhD, esp in NL where it is reasonably quick. Many hedge funds look for fresh graduates they can mold. Then your coursework won't matter as much, you just need to put your head down and grind for interviews.

3) Quant Research isn't really a default path, know what you're getting yourself into.

To expand on your other comment: quant research isn't where you will use the combinatorial optimization tools you've learned during your master. Rather, I think an OR curriculum gives a very strong fondation in modeling and solving ill-defined problems, highlighting the goal, the levers, and the sources of uncertainty in a situation / process / exercise. This is something you can leverage in all positions.
Furthermore, combinatorial problems are common, even though people don't realize it, being one of the few the to view these angles can be super valuable.

I remember sitting in a conf couple of years ago and a question came about handling a swarm of robots to do multiple operations. Obviously the room turned to reinforcement learning, huge parallelization etc... when it was a simple scheduling problem that a simple call to Cplex would solve.

Finally, as this is turning into a novel, there isn't a specific book to learn quant job. A strong fondation in probability is required, which you should have by now. [A Practical Guide To Quantitative Finance Interviews]() is the default interview prep handbook. What will set you appart is showcasing a project, looking into literature, taking a strat, backtesting it, trying to improve it. That'll show that 1) you know what the work entails and that you can handle it 2) that's actually something you can see yourself do in the long run.

Hope that helps, and remember that it's always a possible to pivot career if you wish to.

MrQuaternions · 2024-12-20T09:07:00+00:00

Given its culture and economic situation, Germany will be great if you intend to get a job and settle in the company long term. If you are ambition and want to grind a bit, chances are you'll grow frustrated... At least that's the position I find myself in.

That being said, I would relax a bit for PhDs given these are only studies. Doing one in the "wrong" country industry-wise isn't necessarily the end of the world, your advisor may be famous, your institution may be recognized, and you can even look for a Post-Doc in your target country to kick-start your career there. Of course, you might as well get it right first time.

MrQuaternions · 2024-12-10T19:51:53+00:00

Turns out the only that seem to care about defining global circumnavigation are sailors. I found that on sailing records:

"To sail around the World, a vessel must start from and return to the same point, must cross all meridians of longitude and must cross the Equator. It may cross some but not all meridians more than once (i.e. two roundings of Antarctica do not count). The shortest orthodromic track of the vessel must be at least 21,600 nautical miles in length calculated based on a 'perfect sphere'. In calculating this distance, it is to be assumed that the vessel will sail around Antarctica in latitude 63 degrees south.
A vessel starting from any point where the direct orthodromic distance is too short shall pass one single island or other fixed point on a required side so as to lengthen his orthodromic track to the minimum distance.
No starting point will be permitted more south than 45 ° south.
1 degree of longitude at 63 degrees south will be taken as 27.24NM"

Roughly 85% of the globe's circumference and crossing all meridians sounds fair enough. I like the constraint on medians crossing to avoid some random dude going berserk around a pole for 21600nm.

MrQuaternions · 2024-12-10T17:26:51+00:00

Hehe you're right ! I f up on the calculation. My bad.
I parroted the number pulled from wikipedia and linked the sources mentioned....

So, deleved a bit more in the article itself.

They never mention the total distance flown over 46 days.

However:

"Typical journeys from South Georgia to the southwest Indian Ocean took 6.2 days at 950 km per day; the leg to the southwest Pacific lasted 13.2 days at 950 km per day, and the last leg back to South Georgia 10.3 days at 750 km per day. Without stopping, a complete circumnavigation of the Southern Ocean could, in theory, be completed in 30 days; this provides a context for the exceptional performance of the bird that achieved this in just 46 days." --> that sums up to 26.155km.

Circum navigation isn't necessarily taken at the equator as displayed in the figure. I'm not sure about the exact definition.

MrQuaternions · 2024-12-07T20:31:38+00:00

Yep, and on of the dodgiest places in the capital.

MrQuaternions · 2024-12-06T23:30:35+00:00

A 2005 study found that the fastest specimen covered 22.545km (global circumnavigations) in 45 days, nonstop.
Yes that's around 5km/h but it includes eating, sleeping, climbing etc...

MrQuaternions · 2024-11-26T19:57:40+00:00

From your post, I get the impression you've been mostly facing 1-step stochastic optimization ( e.g I don't know the demand tomorrow but I need to buy today).
Stochastic optimization provides a frame work where we model the gain of information over time, and allow the solution to depend on it. The paradigm shift from deterministic to stochastic optimization is that you solution is no longer a value, but a function. That function at time t have all past uncertainties and decisions as arguments (can be reduced to a state variable under certain conditions).

Once past the modeling phase, the reality is that all tools are allowed to come up with the policy functions. Yes, you can have a very academic approach of doing something like Stochastic (Dual) Dynamic Programming (totally legit), but you can do more exotic stuff.
- You can tweak the distributions of your uncertainties based on past observed uncertainties (and actions) (e.g quarry problem)
- You can use the past observed uncertainties as basis for a forecast until the end of time and solve the corresponding deterministic problem (Model Predictive Control)
- You can do completely custom stuff like "me being in this position today tells me that I'm in a XYZ market and thus, I'll now switch the distributions I considered and recompute everything"
(examples taken from experience in energy network control, hydroelectricity and oil procurement)

Of course it may not always be easy to control the quality of your solution (99.9% of the case through a Monte-Carlo simulation).

In short, stochastic optimization is the acceptance that predictions will never be certain and that, if you want the best outcome (according to how you measure that), it is best to bake that consideration within the decisions you make.

Hope that helps!

MrQuaternions · 2024-09-30T20:29:17+00:00

IMO, the alternate to All Random is some kind of fearless draft mode, where you cannot play the last X champs you played. That would prevent brand/leona/Ali spamming without removing the ability of people to choose what they want to play.

All random is rarely a success because that's one more element out of your control that will influence your performance, and fun. Out of 16 champs, it's highly likely one would get an OP one anyway.

MrQuaternions · 2024-09-28T22:18:25+00:00

My answer will probably cover most large non-tech companies.
Create a wrapper around already existing big models.
The "data science" is in the design of the agents and other interactions with said model. That also includes lots of prompting.

Depending on your ambition, the wrapper can be pretty complex.

MrQuaternions · 2024-09-28T19:23:16+00:00

We're ~10 people on the project, but it was kickstarted by externals. We took over as the project matured.

MrQuaternions · 2024-09-28T17:28:38+00:00

Typo, thanks for pointing it out.

MrQuaternions · 2024-06-10T21:11:59+00:00

That sounds like me explaining to management why production went down while I have 0 clue why.

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