Latest reviews

I joined the program in 2023. My undergraduate background was in Mathematics.

The University of Washington runs on a quarter system, meaning there are 3 academic terms in a school year of 10 weeks each. This allows you to hit the ground running and broaden your exposure for more courses and topics. At the same time, it is very easy and straightforward to register as a part time student to allow for more flexibility in your time commitment.

One aspect I really want to applaud of the program is the academic rigor and quality. At least two members of my cohort thus far have gone on to pursue doctoral study. This program is not only technical coursework as a means for a career, but instills a standard of theory expected to produce novel work. A close peer of mine pursued the undergraduate major from this program. He remarked his new graduate program now has been admittedly easier than his CFRM curriculum. Matt Lorig, a faculty member, has written his own course notes for a number of courses, and they are very intuitive and elegant. (I wish I had his LaTeX template)

Another phenomenal aspect of the program is the chance to pursue the thesis option. Students are given the opportunity to propose a research project and pursue independent study directly under a faculty member, to then form a thesis committee and defend it. You will have the chance to present a new R library or dive deeper than the coursework to answer other questions in the field.

Seattle is admittedly geographically distanced from the main centers of quantitative finance, yet the career services support remains strong and effective. There is a dedicated career services manager for the program specifically. I had the chance to intern at Parametric Portfolio Associates, which is a Seattle-headquartered subsidiary of Morgan Stanley Investment Management. I had a general question about my internship while I was still incoming, and the career manager actually took it upon herself to reach out to my talent acquisition team on my behalf to get full clarity. This level of attention is extended to all students, and it is up to you to take full advantage of it. While an intern, I noticed I saw a CFRM mug on desk as I was walking through the office. I thought, huh, that's pretty neat, there's an alumnus here, too. I then saw five more mugs across other desks in the office. I then realized that was no coincidence. The City of Seattle also takes on graduate financial research interns, where the last two at least have been program students.

The career services manager prepares weekly workshops and seminars offered for students to attend. These have ranged from local guest speakers and panel discussions, as well as resume building and interview preparation. Every year, the program has about three dedicated tables reserved at the CFA Society Seattle Economic Forecast Dinner. I have had the chance to attend two, they were phenomenal opportunities to meet others and represent the program.

By far my favorite class in the program, and likely ever, was CFRM 523: Advanced Trading Systems, taught by Dr. Ben Hansen. Rather than just teaching you a skill or concept as a means to an end, the focus of this course was to understand how to rigorously apply an academic approach to quantitative finance, treating trading strategies as a scientific and repeatable process. I not only got to learn derivatives pricing or credit risk management, but also the level of scrutineering that I should have when conducting this work.

On that note, one point of improvement that I have is the elective offerings. Naturally, a majority of the cohort gravitates towards choosing a single elective each quarter. This makes it difficult to offer a more diverse pool of course selections, given student interest and course section minimums. Another strong CFRM employer in Bellevue does power and energy trading, it would have been nice to see an energy markets course offered. I now do research at a single-family office and its venture affiliate designing synthetic exposures and liquidity provision, and would have enjoyed taking the course on blockchain. While admittedly niche topics, these industries remain prevalent in the Seattle area, and instructors from them would strengthen the depth of the program. That said, there are a number of approved electives you are allowed to take from other schools and departments.

There is so much to do outside of the classroom. For UW's Graduate & Professional Student Senate, the Department of Applied Mathematics has two seats, and they give the chance to offer one to a CFRM student. This is an additional leadership opportunity that led me down an entirely separate rabbit hole and gave me a year of on-campus employment. Jane Street has run their Estimatathon competition here, and I have attended a number of their presentations on-campus. There is also the algorithmic trading club, a registered student organization dedicated to quantitative finance.

Overall, I found the program to position me well from both an industry and academic perspective. Seattle may be far from these industries, but they make up for this through the care and attention from the faculty, career, and advising support. There is a very cordial atmosphere in the program and department.

This is a fantastic program, definitely one of the best quant finance programs in the world, outstanding industry reputation and powerful alumni network!

Career
The career support is incredible. From the very first week on campus, you're meeting alumni/recruiters/people from top hedge funds, prop firms, and banks who are there to share insights about their companies and recruitment processes. Career services reviews your resume (and provides great tips to get it right) for a resume book that goes out to many of these employers. Many employers will directly email candidates to schedule interviews. The program also offers interview prep and actively connects students to alumni and firms.

Coursework
The program is extremely flexible. You can choose electives from a wide range of departments (Operations Research, Stats and ML, CS, Econ, Finance), allowing you to really focus on what you're passionate about. The professors are top-notch, and many are practitioners who know exactly what the industry is looking for, which is a big plus. There's also a strong focus on non-directly CS/ML topics, contrary to other programs.

A slight downside is the core coursework. I found some of it to be a bit too simplistic or not directly "useful" for the industry. Some courses also cover too much material condensed into one, and I think the program would benefit from giving students the chance to choose more in-depth courses on particular subjects instead.

Cohort & Culture
The small class size makes it very easy to make friends. My fellows in the cohort were really friendly, interested in getting to know people, and always willing to help. This collaborative environment is why the Princeton network is such a big deal, you'll always find someone willing to help you with a particular topic you're interested in also with people in the years above.

Other
My only other suggestion is that I think it would benefit more from a length of 1.5 years instead of the current two years.

Very strong and diverse quantitative program. You can dive deep across many different fields within quant finance. Some exams and courses are quite tough but worth the hustle for such a competitive progam.

Expect technical courses that probe both math and code. Exams are tough and reward generalization: instead of repeating class problems, they push you to extend the theory to unfamiliar situations. Plus, a strong lineup of data-science classes lets you dive into cutting-edge machine-learning electives.

Part 1- Program Design: Program Size & Courses & Resources & Outcomes

This is not a traditional MFE program, this is a applied math program with a bit financial elective. Student need 45 credits to graduate. In general, each course count as 3 credits and the required finance related credits are only 9, approximately 3 courses. Other course requirements are about CME, Computing, Data Science, Practical. The CME requirements are about CME courses, which are the PhD level applied math and also the requirements for PhD Qualification exam. Computing requirements are about fundamental CS and Advanced CS, which includes data structure & algorithm, operating system, parallel computing, CUDA etc. The data science requirements are mainly stay around at Stanford's CS and Statistics courses. The CS courses are those famous Stanford public open CS course, while Statistics courses are about those courses for Statistics PhD Qualification Exams. The Practical requirements are quite chill, they can be some 1 unit seminar, or some research project/assistantship units or some easy financial practical courses. You can see, the program is very math intensive because many required courses are for PhD Qualification Exam among the Math, ICME, Stat departments. That's why the MCF or the whole ICME department prefers the students that have strong math background.

The class size for MCF is super small, only around 10 people each year. According to my experience, the admission difficulty in ICME MS could rank like this: MCF > General > Data Science. I am not putting the image science here because the class size is too small, only 1-3 student each year, so it's too hard to estimate. Moreover, may be 2 out of 10 spots are reserved for Stanford undergrad, which is called the co-term program.

Very good resources at Stanford, include funding, free foods, trips, and other opportunities like networking. Also Cheap tuitions. I know many ICME student get Teaching/Research Assistant at school, which can fully cover the tuitions and getting $3-4k salary each months, but it's not easy to get these since it's pretty competitive. Every week, we do have free foods like bagel or pizza in our office. Each year, we also have free trip for all ICME MS and PhD students, this year we went to SF together. Every Friday, we also have Friday's beer, which is the time to meet with cohorts, alumni, and professors. For tuitions, this is much cheaper than other MFE programs, even cheaper than Baruch.

Many students may choose to do PhD after graduation due to its rigorous training in applied math and many research opportunities at school. I am the student that admitted for fall2024, I can say there are around 3 of my cohorts have a plan for PhD, so they do not participate any intern/full time job search. I also have the plan for PhD, but my return offer is decent enough to pull me away from doing a PhD at Stanford. Overall, ICME provides very good resources for students to PhD.


Part 2 - Career Service and Job Placement

Almost not the career service as what you see as other MFE programs. The only typical career event is ICME career fair, only open to ICME and Stat students. The companies in the career fair are not that decent as you may think (like HF, Props, etc.), but they are still pretty good and high pay, include Vanguard, Upstarts Apple, and some Labs.

One possible way to get jobs via school's career service is like go to the general career fairs like Stanford CS Forum, Stanford Career Fair. These events will involved many companies includes famous tech companies/startups, also those HFs, Prop shops, etc.

Another possible way to get jobs is go to the company events. Each week I remember there are a lot of companies (tech, startups, HFs, Prop shops) info session, events, activities, networking on campus, some of them provide Stanford specific pipeline for application. Other than job application, you can also network with many people for many companies, also get free food and swags!!!

Job placement is pretty good, I don't know the data for internship right now, it seems almost everyone get pretty good intern offers except those plan to do PhDs (yes! they did RA at school during summer). For full time, I already know 2 cohorts get return offer that has number ~500k+ (damn! very good numbers!), which seems not rely on those career service, only the strong skill and past experience.


Part 3 - Life

Very very goooood campus experience!!! Beautiful Campus! Many many campus activities! Many many funs! Best campus experience in the world!

Princeton’s MFin combines the most attractive features of a top master’s program: a small and highly selected class, the best alumni network and reputation, and a uniquely rich academic environment.

The small cohort definitely helps in getting to know each other better and developing friendships that last over time. This is also reflected in the alumni network (all over the top banks, hedge funds, prop trading firms, etc.), which makes for valuable connections. It goes without saying that the professional excellence of alumni is tangible proof of the reputation the program holds in the industry. The career service is also extremely supportive: with tons of experience, they actively connect students with firms and alumni and organize events. Most importantly, Lindsay is very helpful in navigating the recruiting process and the offers. This makes a real difference, especially during internship and full-time job searches.

The curriculum is very flexible and allows specializing in your area of interest (whether it’s more computational finance, quantitative economics, etc.). In general, classes are small, so that leaves space for much interaction between students and professors, benefiting the learning process. Furthermore, the homework assignments are hands-on and very helpful for students on their way to their summer internship.

Finally, beyond the program itself, Princeton offers an exceptional environment. The campus is gorgeous, and you have the opportunity to meet with brilliant students from all disciplines and diverse backgrounds. A big part of my experience has been making friends also outside my program and taking classes in other departments for personal curiosity. The university really provided the ideal setting to focus, learn, and grow. Would definitely recommend and redo the program, time and time again!

This Master’s program is jointly offered by the University of Zurich (UZH) and ETH Zürich. The curriculum includes courses from both universities, with the technical and mathematical subjects mainly taught at ETH, and most finance-oriented courses offered by UZH. Since students can choose from a range of electives, the proportion of time spent at each university varies individually. However, it is not possible to complete the degree without taking at least some courses at both institutions.

ETH Zürich

The quality of lectures at ETH is truly outstanding. Every lecturer I had was highly knowledgeable in their field, and many conveyed genuine enthusiasm for the subject matter, which made the classes engaging and inspiring. I was particularly impressed by how many lecturers cared deeply about teaching quality — something that, in my experience, is not always common at European universities. Having completed a STEM Bachelor’s degree at another major European institution, I can confidently say that ETH sets a remarkably high standard for both academic rigor and teaching commitment.

In terms of difficulty, ETH exams ranged roughly between 5 and 9 on a scale of 1 to 10, with some courses I did not take likely reaching a 10. Exams were generally fair and well-graded, reflecting both the course content and the students’ understanding.

University of Zurich (UZH)

The quality of lectures at UZH varied significantly. Some lecturers seemed disengaged and uninterested in teaching, while others were motivated but lacked the ability to deliver clear or structured lectures. Although there were a few excellent courses, I also encountered some of the worst lectures of my academic career at UZH.

Exams at UZH were generally easier than at ETH, ranging from about 2 to 6 in difficulty, though there were some exceptions. Unfortunately, grading was often inconsistent or poorly designed. Many assessments relied on arbitrary grading schemes that seemed intended to simplify marking for teaching assistants rather than to accurately measure student understanding. The few difficult exams were often not challenging because of the content, but because lecturers deliberately made them excessively long or included obscure details only mentioned in passing during class — practices that seemed more punitive than pedagogical.

Overall, I was not satisfied with the UZH component of the program. While I did learn a great deal from the better courses, the weaker ones fell short of the standards expected from a program that markets itself as prestigious. The degree coordinators and academic staff were consistently kind, responsive, and supportive. In contrast, interactions with the broader university administration were often cumbersome and discouraging. The level of bureaucracy was high, and staff outside the program office were generally not accommodating and occasionally impolite when it came to handling uncommon situations such as credit transfers or other non-standard requests.

Career Prospects

The program administration makes commendable efforts to build connections with industry partners and facilitate internships, master’s theses, and networking opportunities. Compared to typical degree programs at ETH, UZH, or other large non-UK universities, this is a definite strength. However, when compared to other specialized programs in quantitative finance, the career support feels somewhat limited. Securing a position outside of Switzerland requires additional effort, as the program’s industry reach is mostly local.

Overall Impression

Overall, I would recommend this degree. It offers excellent learning opportunities and some truly outstanding lectures, particularly at ETH. However, students should choose their courses carefully — ideally consulting those in the year above for advice. Tuition fees are relatively low, although living in Zurich can be expensive. Career opportunities in Switzerland after graduation are good, but internationally they are somewhat limited.

Course:

The program offers a mix of finance, computer science, and mathematics courses, with a strong emphasis on theoretical foundations and financial pricing. There are only a few compulsory courses, while most are electives. Students have the opportunity to enroll in PhD-level courses, which provides academic depth for those interested in advanced or research-oriented topics.

Career Services:

Career outcomes are generally strong. The career service office organizes information sessions, offers interview prep, and connects students to the broader university career fair, which includes a larger pool of employers from different sectors. Career support tends to focus more on information and guidance rather than hands-on job placement.

Peers and Community:

The student cohort is highly capable and diverse, both academically and professionally. While many students aim for quantitative roles, others pursue careers in consulting, asset management, risk, or data analytics, making the overall career paths more diversified. The environment is collaborative yet competitive.

The course has good curriculum and for the most part very good quality of teaching with lecturers being not only accomplished and experts in their fields but are also engaged and easily available. Teaching is definitely the highlight of the programme.
Careers service is not very good. It exists, and is easily accessible and at times organizes interesting events and sources good employers. However, there is no dedicated team for quants, which not only requires specialised knowledge but is also very competitive to get in so the advice and careers support should be stron in order to be effective. Unfortunately, it is probably more effective for regular finance students.
When it comes to career outcomes, some top candidates get very good placements, but for many struggle to find job and end up in mediocre placements

The Master’s program in Quantitative Finance at UZH and ETH Zurich is highly selective, but for good reason. It’s an ambitious and rigorous program designed for mathematically inclined students who want to transition into finance. For anyone with a passion for mathematics, it feels like a playground: you get to dive deep into topics such as stochastic processes, financial modeling, derivative pricing, and optimization theory.

At the same time, the program manages to balance theory and practice very well. While it offers courses with strong mathematical and theoretical foundations, it doesn’t lose itself in abstraction. Students can also choose from more applied, hands-on courses, allowing them to benefit from the best of both worlds provided by two of Europe’s leading universities.

The professors I’ve had were outstanding: highly knowledgeable, engaged, and experts in their fields. Many have strong connections to the finance industry, which brings a pragmatic touch to their teaching and bridges the gap between theory and real-world application.

The curriculum is well structured and diverse, offering courses that range from pure finance to pure mathematics, and everything in between. The program also enjoys the support of numerous industry partners, which strengthens its practical relevance. From my experience, career opportunities in Zurich after graduation are very good. Non-EU students may face additional challenges due to local regulations, but that’s independent of the program itself.

Overall, I would highly recommend this program to anyone who aims to “become a Quant”.

The program is really good, really competitive and it is for sure one of the top programs out there. There are many positive things to it and few negative ones.

Positive side:
The program is very rigorous and the fundamentals part is really heavy, you would have to prepare yourself to get a functional analysis course compacted into two or three weeks if you want to learn about machine learning in finance. You are not aware on how much you’re learning because most of the courses are loaded with material and the content is really advanced. Once you get past the first semester or second you will see some results if you enrol into a Hackathon and/or get an Internship and you get to compare yourself with students from other universities. Another positive part of the program is that you get to surround yourself with a lot of very smart and well prepared people which make it easy to pick up good habits. Also, you would be living in Zurich, which is a city that promotes a very healthy lifestyle, when the exams season comes the daily day routine consists of doing some sort of sport, studying all day and at the evening going to the lake to relax from stress. Students can also chose to do their thesis in any university in the world and because of the prestige of ETH you can get replies from professors from all of the top ranking universities, but many people still prefer to do their master thesis with the help of some company in Switzerland to work on the possibility of a job with them after completing the program. Most of the professors are very well-known, and some even have partnerships with blue-chip banks to develop new technologies.

Negative side:
It is well recognised within the industry but I really believe there should be a person in charge of helping students with career services. There is little to no help at all in this matter. The non-EU citizens struggle with getting a job in Switzerland because of visa requirements. This said, after graduation everyone still has the possibility to go to the UK and work there because of the HPI visa which you can get after completion of the program. Sometimes many people chose to go back to Switzerland because of the high quality of life after working in the UK or some other country for a couple of years. The job market is really tough, but I have heard from people within the industry saying things similar to: “we are just waiting for you to graduate so you can work with us”.

I would definitely recommend the program, also because of the cost. For what you pay and you get it is possibly the best option.

- Landed an internship within 1 month and a full-time job 2 months after graduation.
- Course-based program with instructors mostly from the financial industry — they share real-world insights and practical experience.
- Valuable guest lectures and alumni sessions that strengthen learning and networking.
- Mock interviews and excellent employer connections through the program coordinator.
- Curriculum updates: Stochastic Calculus replaced by Dynamic Data Science (focus on portfolio optimization research).
- Quantitative depth slightly reduced, but course design remains student-friendly and practical.
- Second term emphasizes group projects and presentations — helpful for communication and teamwork skills.
- Around one-third of students land strong jobs, mostly in quantitative risk and pension fund investment roles (fewer pure “quant” positions).

Very informative course with helpful TAs and community. Programming in Quant Finance can seem very overwhelming in nature but the course provided the right foundations and a solid base to continue navigating the space.

The MS in Mathematics in Finance at NYU Courant is an exceptional program that I strongly recommend. Coming from an Ivy League background in mathematics and statistics, I found that this program truly deepened and extended my quantitative foundation into the practical domains of finance.

The curriculum bridges rigorous theory with industry application. Courses in stochastic calculus, C++, machine learning, and financial engineering helped me understand topics like FX option pricing, interest rate modeling, and regression analysis at a much more advanced and applied level than in my undergraduate studies. The program’s structure ensures students gain both analytical depth and implementation skills that are directly relevant to quantitative research and trading roles.

In addition, the program’s strong industry connections, small class sizes, and highly engaged alumni network provide a clear edge in interview preparation and job placement. Guest lectures, networking events, and career workshops all focus on what is currently relevant in the quant industry, which made me feel well-prepared and confident for interviews.

Overall, NYU Courant’s Math Finance program combines academic rigor with real-world readiness. It is ideal for anyone aiming to build a serious quantitative finance career.

Coming from a non-quant background, joining the NYU Mathematics in Finance program was a major turning point for me. The program’s strong industry connections and tight alumni network were instrumental in helping me break into the quant industry. Before starting, I didn’t even have a clear idea of the differences between the buy side and sell side; now, I feel comfortable navigating nearly all major domains in quantitative finance—from trading and structuring to risk and data science—thanks to what I learned in lectures, program events, and guest talks by industry professionals.

Most courses are taught by practitioners who blend rigorous theory with practical insights and often touch on real interview-style questions, which makes the learning immediately relevant. I also appreciate the small class size: it allows for closer faculty interaction and easier access to alumni, which greatly enriches the learning and career-building experience.

In short, this program provides the structure, community, and exposure needed for anyone serious about transitioning into quant finance.

As a financial engineering student that knew very little about finance before coming to Stevens, the program has prepared me for a career in the finance industry. The Master´s program is mathematically rigorous, and all professors are excellent and will help you out whenever you needed. There are many scholarships you can take advantage of (CFA L1, FMR L1). The program director will help you get around with networking, classes, and which path to take. Got a great job offer after graduating, thanks to that networking. I would absolutely recommend this program.

What I liked:
1. The Course itself.
I believe it is well structured, a mix of everything maths, coding and finance which I think is very nice. I also liked that you get exposure to some modules that I honestly didn’t even expect, like Decentralized Finance and High-Frequency Trading. These are quite modern topics, and I thought it was really interesting that they were part of the course.

2. Lectures
I honestly believe the lectures/seminars were generally very informative and interactive. Different from what usually university lectures are. I remember some modules like Quantitative Risk Management, Stochastic Control, Stochastic Analysis, and Fixed Income being really good.

3. Industry Exposure
I think we had companies come over almost every week to give talks. It was nice because you got to meet people from different areas of finance (risk, trading, research etc.)and hear about what they actually do. It gave me a clearer idea of what kinds of roles are out there and what to expect if you want to go into industry after the program.

4. Dissertation
You have the option to do your dissertation with a company. I personally didn’t get the chance to do mine in industry, but I know quite a few people who did. For them, it worked out well and many ended up getting full-time roles afterwards through those connections. So that’s definitely something worth considering if you get the opportunity.

What I didn’t expect:
5. Workload
The course is very intense. There’s really no way around that. You’re learning a lot of material in a short amount of time, and at some point, it can feel a bit overwhelming. I remember in the second term, we had around eight modules going on at the same time. It’s hard to balance everything, and honestly, you don’t really get much time outside of studying. But then again, it’s Oxford I guess that’s something you expect to some extent.

6. Career Support
The program gives you quite a bit of exposure to companies and industry people, which helps. But when it comes to actually preparing for interviews, especially quant interviews, you’re mostly on your own. You get the background knowledge, but you still have to put in a lot of work separately for coding, brainteasers, and the kind of technical questions that come up. It’s definitely a lot to handle alongside the coursework.

The course altogether is good. They teach Stochastic Calculus, FinStat, FinEng and other relevant basic Quant subjects. But primarily the focus of the program is mostly towards the risk management side of the normal/quant side mathematically in depth.

Alumni is not that supportive along with career service is terrible. They take all the finance streams students to all the career events, just good for networking. International students are not much appreciated in the market despite paying (2x), the fees. The quality of studies is really good, module leaders are great but again getting a job post program is tough.

I mostly agree with the previous two reviews in this thread, so I will write my review as an addition to what has already been described there.

The biggest strength of this program is its broad curriculum, composed of highly specialized courses that allow you to go in-depth with the topics of your interest. There are about 80 courses available each year, and many more from other departments (such as informatics and electrical engineering) can be taken but are not listed on the main course list. A significant number of courses are taught by "professors of practice". These classes often consist of practical projects. 7 out of 17 courses I took were of this kind, and they were my favourites. In general, 16 out of 17 courses I took during my studies I would genuinely recommend to others.

This flexibility in curriculum is a double-edged sword. Poor course selection will spread one's focus across multiple topics, and the student will fail to specialize. The Swiss job market is highly competitive, so without a strong specialization, a fresh graduate may have to look for a job elsewhere.

The Alumni Club (ZQFA) is a central for networking and a very helpful source of information. One can get great advice on one's specialization, courses, industry trends, the job market, etc. Implicitly, the club serves as a career service, and it does so very well.

Aside from the highly competitive local job market, another drawback of this program is that exams at UZH are held in June, while ETH offers some exams in early June, but most are in August. Therefore, the program offers less time for summer internships. Nonetheless, many of my peers secured summer or off-season internships, mainly in London, and later landed full-time jobs at those companies.

The PMP program is an excellent opportunity for future portfolio managers. Three teams of students manage a real portfolio (around 2 mil. EUR AUM, the last time I checked), and take the whole set of courses for portfolio management.

Another great specialization is in actuarial sciences/insurance mathematics. ETH Zurich and Zurich in general are among the world's leading insurance hubs. All of my peers who specialized in this field reported exceptionally positive experiences, especially those who landed a job in Zurich.

Last but not least, if you are interested in an academic career, UZH and ETH should position you well for almost all major areas of research in quantitative finance.

The program is overall well-rounded and fast-paced but very flexible. It can be finished in 3 to 10 semesters, probably most students take 4 or more, especially if can't find the job they want or want to pursue a phd and want to further specialize in an area.

We can divide the courses in 2: ETH and UZH
ETH: very theoretical courses, with great variety of electives/external electives available (you can take courses from other departments, so you have access to MATH/CS/..., even electrical engineering department if you want to do a course on power trading for example)
UZH: level of teaching of core courses is lower, however most of applied stuff you'll do comes from seminars/3 credits courses found here or assignments you get at ETH courses. Good amount of practitioners from different parts of finance.

Being part of UZH also gives you access to apply for the PMP program www . cpm . uzh . ch , which is great especially for starting to get experience and obtain contacts with the industry.

Regarding events ETH is especially great as there are many events with prop trading companies and hedge funds all around the years. Though the chances of getting a interview out of them is very limited even if you were to win them...
On the other side I can recommend UZH finance club which hosts free brown bag lunches with important companies all around finance (PE/IB/AM/Banks and Central banks/...)

This said, especially the first semester doesn't give you free time to prepare for internship applications adequately and the career services aren't specialized for our masters. You still have access to ETHgethired and UZH services though. I can vouch for the first one in particular to get a first contact with smaller swiss hedge funds that advertise their positions there (which only hire from EPFL/ETH most often).

As mentioned by others, Switzerland makes it hard for non-Europeans to work after graduation, however is still possible with some work and luck.
In general, for quant positions, especially after the merger ubs-cs, Switzerland remains small in terms of opportunities, but very rewarding. Except for commodities, where Geneva is a major hub (though in that case, EPFL master may be a better alternative due to proximity).

Overall happy, and would choose it again, considering that by working part-time during my masters, I paid both tuition and living expenses.