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Here is my personal experience:

1. Faculty

In my experience, the program’s faculty have been excellent. The professors I’ve worked with are knowledgeable, approachable, and often incorporate insights from industry and research into their teaching. Their willingness to engage with students has been one of the strongest parts of the program for me.

2. Teaching and Coursework

The curriculum is mathematically rigorous. Many courses go deep into topics such as convex and non-convex optimization, stochastic calculus, and multivariate statistical methods. This has been extremely valuable for building a strong foundation in the quantitative side of finance.
That said, the level of technicality also means that a significant amount of learning inevitably happens through independent study. Some instructors use Zoom-based lectures, which works for many students, though I personally find in-person discussions more effective. Overall, the program rewards students who are proactive and comfortable taking ownership of their learning.

3. Funding for PhD Students

From what I’ve observed, funding opportunities for PhD students in Quantitative Finance can be limited. Securing full support often requires finding a specialized internship or industry placement, which can be competitive. Faculty members do try to help by recommending students to smaller quant firms, but opportunities are not guaranteed, so prospective students should be aware of this aspect.

4. Career Development Support

Career development is an area where students need to be highly self-driven. Unlike some other programs with established placement pipelines, there isn’t a structured matching or placement process here. Most students take the initiative to apply widely and network independently. While faculty may provide guidance when asked, success largely depends on the student’s own efforts.

positive: smart and nice cohort. Overall a good program to get a taste of the different areas of quant finance. The course offering is very interesting, particularly the electives. It was a pleasant experience overall.

negative: could do much better at prepping for the interviews and the professional transition. Could do better at explaining the different kinds of structures in quant finance, and what a quant would do (and NOT do) on each different kind of role. Some variability in the quality of teaching.

In terms of content, it matches my expectation, as I did a nice amount of digging beforehand but the teaching itself has exceeded my expectations. Lecturers lead by intuition which makes it far more interesting, and also easier in application.

The module selection is fantastic. We can take 4 optional modules but you’d want to take 6/7! Super interesting. I was also pleasantly surprised that we could audit these modules of interest if we didn’t choose them.

Our individual academic tutors are also super helpful. Always happy for a call and to look through their contact book for summer project pairings. Although there hasn’t been a lot of career fairs or efforts to talk about the future, if you make a little effort yourself to meet with lecturers or your academic tutor, you’ll find all that you’re looking for.

There is a warm feeling around campus, and my cohort is supportive - always happy to collaborate!

Overall, feel grateful to be here!

I was admitted through the early admission round, where the process was highly selective and technically challenging. The decision was released in early November, giving me ample time to gain internship experience at quant funds and better prepare for the program. This early start is a major advantage compared to many other programs that release results in the spring.

Coming from a non-math and non-CS background, I found the curriculum extremely effective in building a solid foundation in mathematics, statistics, and programming. The courses are rigorous, well-structured, and closely aligned with industry needs and interview expectations. Many instructors are either leading practitioners or experienced academics, and they put remarkable effort into their teaching. Thanks to the small cohort size, students receive abundant attention and can learn directly from top professionals in quantitative finance.

The career service is by far the strongest part of the program. The program director and faculty genuinely care about every student’s progress, offering continuous guidance, one-on-one mentoring, and tailored advice for both internships and full-time placements. Their close connections with leading firms across the industry ensure that every student receives the exposure and support needed to succeed.

Finally, the Baruch MFE community is incredibly collaborative and tight-knit. Alumni - whether they graduated one year or ten years ago - are always willing to offer insights and advice. This strong, generous network makes the transition into the US quant industry much smoother.

Overall, Baruch MFE provides unparalleled career support, top-notch instruction, and a uniquely strong community culture. It is without doubt one of the best starting points for anyone aiming to launch a successful quant career.

MQF is a program which can build you soild foundation in quantitative finance. This program is well known in Canada and you can access decent alumni resources to help you get a job after graduation. However, it reputation is quite limited outside this country.

Pros: Very cheap, rigorous, famous in Canada, adaquate almuni resources in Canada.

Cons: High study load, forced to learn useless stuff if you found your career direction, quite theoretical, not famous outside Canada.

Pros:
-Technical courses where you learn all essential material to start a career in quant finance
-Cheap Tuition and great value: you get a good Uni name and good education for a fraction of the cost of similar programs
-similar living cost to London
-competitive profile within Switzerland if you are EU
-« some » London and Amsterdam firms and banks screen MFE students for interviews
-flexible program and you can extend studies to apply for more internships

Cons:
-Very few quant jobs in Switzerland and if you are non EU, you have 5-6 opportunities at best
-If you have 0 experience before the end of study internship it will be hard to get interviews
-EPFL Forum (career fair) opportunities are weak
-Career Service is very weak and provides very little guidance as students are expected to do no internships before the 6month end of study internship which is very impractical

Advice:
Do the program, fight to get a summer internship in London on your first year and prepare interviews on your own

The ETH program is somewhat similar with no internship requirements so would recommend EPFL MFE over ETH

Definitely a lot of work, not a lot of time to do side-projects if you want to have to a social life. Idk about admission for international students but if you did your bachelor at EPFL its ez to get in. About the courses, there is some math so thats good but I recommend taking electives where you program a lot since there a few courses where coding is important in the main branch. + Profs are great!

I finished this program in 2025 and figured I'd share my experience since I found these reviews helpful when applying.
The stochastic analysis module is the highlight of the program. The professors know their stuff and have actually worked in the industry, which makes a difference when they're explaining concepts. You'll cover both theory and implementation, though be prepared - it's properly difficult.
This isn't an easy degree. It's basically PhD prep, so expect to spend most of your time studying. Some modules will feel overwhelming at first, but you do eventually get through them. The workload is heavy throughout.
The program covers math, stats, CS/ML, and finance fairly evenly. You won't just be doing derivatives pricing - there's a decent amount of programming and data science mixed in. Some students struggle with the coding if they come from pure math backgrounds.
Class size is about 20 people, which is good for getting help during office hours. Most students come from math, physics, engineering, or CS backgrounds. Some are brilliant, which can be intimidating, but it pushes you to keep up.
Career services for our 2024/25 cohort wasn't great initially - generic university careers events that weren't relevant for quant roles. After we complained, they started organizing MFE-specific events with hedge funds and prop shops, which helped. The university societies do run networking events with banks and funds if you're proactive about attending.
The Warwick name does help with applications. I got interviews at most places I applied to, both UK and international firms. That said, you still need to prep hard for the technical interviews - the degree alone won't carry you through.
Overall, if you can handle the workload and want proper quant training, it's a solid program. Just don't expect it to be easy or to have much free time during the year.

[TLDR at the end] I will not go through the aims and objectives of the master’s program here. You can find that on the course website, so please do your own mandatory research to have a basic intuition of the program at LSE that you are paying £40k for - higher if you are reading this in a few years' time.

The program, run by the Department of Mathematics is (was) structured as follows: there will be a 2-week pre-sessional course that aims to introduce some measure theory and stochastic calculus, including, but not limited to, martingale theory, brownian motion, Ito’s Lemma, Girsanov’s Theorem and the Radon-Nikodym Derivative. You will take a non-credit-bearing, informal assessment at the end of the two weeks to consolidate their understanding of the material. I personally did not find the material difficult, as I had exposure to said topics at the undergraduate level, albeit at a shallower level.

Then come the 8 courses that form the master’s program as a whole. For context, I was a student from the 24/25 cohort. The composition of mandatory and optional courses may have changed throughout the years. But during my time, I had to take 5 compulsory courses and only got to choose 3 electives. Now, research the assessment criteria and method yourself, i.e., if it is coursework-oriented or exam-heavy. You can click into each module on the course webpage to find self-contained information, which is all that you need - I want to and will focus on aspects of the program that are not (immediately) obvious.
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The lack of flexibility in the aspect of course selection was quite disappointing, considering that the ‘sister program’, Quantitative Methods for Risk Management (QMRM), run by the Department of Statistics, had way more course choices that comprise the electives portion of the course, at least when I was studying there in 2024/2025. So, QMRM gets 2 points over Financial Mathematics.

I would also like to encourage you to go through the graduate course guides for the program meticulously before selecting your electives based on random decision generators. These graduate course guides are freely available to view on Google, even if you are not a student at LSE and reading this review just for your own pleasure on QuantNet. The 2025/2026 graduate course guides can be found by searching ‘LSE 2025 Graduate Course Guides’.

Now, THIS IS THE MOST IMPORTANT PARAGRAPH IN THE REVIEW. Please get a feel for the available electives for both the Financial Mathematics program and the QMRM program - these two programs have a high degree of overlap, at least when I was a student there. Personally speaking, if you go into this program aiming to acquire the foundational skills for the (quant, AI, ML) industry, then the QMRM program as a whole will suit you much better, as the Financial Mathematics program is fairly theoretical and does not dive into the applications and implementations that are absolutely compulsory for the industry. You just have to compare the elective selections for both programs to verify my proposition. So, important things to do : compare the course structure for Financial Mathematics and QMRM, and hopefully sufficiently validate my proposition.

Note that you do not necessarily have to have chosen a particular course to access the respective course materials. You can audit most courses as an ‘Auditing Student’. At least that was the case when I was studying there in 2024/2025. Obviously audit the courses that you are interested in on top of your 8 modules, not the other way around, as you are still a student, or became a student - and so have courseworks and exams.

Here I list the modules that are in both the Financial Mathematics and QMRM programs that I think are useful, or somehow beneficial in the future : Computational Methods in Finance, Statistical Methods for Risk Management, Stochastic Simulation Training and Calibration, Topics in Financial Mathematics, Quantitative Methods for Finance and Risk Analysis. Advanced Time Series Analysis.

I want to give a special shoutout to the course MA420 Topics in Financial Mathematics and Johannes Ruf, who taught this course during 2024/2025. This module brought me from Level 0 to Level 5 (or any arbitrary numerical figure that you deem appropriate) in working with financial data. For context, before taking this course, I had literally zero exposure to the pandas library, and hence, hadn’t read a single CSV file and hadn’t extracted a column in a dataframe. This course has been my lifesaver.

SECOND MOST IMPORTANT PARAGRAH IN THE REVIEW. I want to justify my choice of headline. Most people tend to expect Financial Mathematics to be a ‘mix of finance and mathematics’. Fact is, the discipline itself is a rigorous exploration of (very) advanced topics like stochastic processes and partial differential equations, and probability theory. You can have a look at the MSc Mathematical Finance program offered by Warwick Business School. On the other hand, the LSE Financial Mathematics program is pieced together by courses from the Mathematics, Finance, and Statistics departments, and each department teaches its segment in relative isolation, resulting in a curriculum that mirrors the misconception (that I pointed out) of a ‘mix’, rather than an axiomatic whole. Hence, I prefer to name this program Mathematics and Finance, albeit seeing some improvements on the holisticism of the program when they incorporated a new module from the Statistics department, ‘Mathematics of Market Microstructure’.

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Other key facts or opinions
Applied AND received my offer in March 2024
Undergraduate Degree : Kings College London Mathematics. Close to First Class Honours when I applied. Performed fairly well in key modules. Transcript available upon private message through my LinkedΙn - we can set up a meaningful discussion.
Other university offers received for the same subject : Kings College London (KCL), Warwick (WBS), UCL, rejected by Imperial.
Conditional Offer : 2.1 on the UK scale (60%)
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TLDR : Choose MSc QMRM (Quantitative Methods for Risk Management) over MSc Financial Mathematics to put yourself in a better position before going into the industry. On the other hand, choose MSc Financial Mathematics if you want to stay in academia and pursue a PhD.

Pros:
1. Hardcore courses with strict standard. The courses include most of the Q-quant content, like derivatives, stochastic calculus, interest rate models etc. Those courses require students to have solid foundation of math. The exams are also hard and strict. Students need to spend quite a long time and understand it deeply to get a good result. It could be tough, but you could learn a lot if you want.
2. Low cost. The tuition is quite low compared with US universities.
3. Good Teaching Assistant. Most courses will have exercises. TAs are from Phd programs. In exercises, TA will describe the knowledge that was not clear in lecture and teach some important exercises in detail.
4. Compulsory internship. The program requires student to take 6-months internship and finish the master thesis in the final semester, which gives students at least one experience to take an internship in master study.
Cons:
1. Less internship chances. Switzerland does not have too much companies for quantitative finance compared with US and UK, which make it difficult for students to find an internship. For non-EU students, its more difficult as they need to apply work permit if they want to take an internship in other countries, like France, Germany, UK.
2. Career Service. The career service is not good. We can find internship chances in the portal. However, these chances are quite competitive, and most companies are not famous.
3. Hardcore courses. This could also be a disadvantage. It occupied too much time for students so they do not have much time to prepare for interviews. The courses are also traditional, which does not cover too much updated content, like ML, DL, AI, etc.
4. Alumni resources. We do not have too much alumni enter famous companies, like investment bank, hedge fund. For what I know, we only have two alumni successfully enter in famous hedge fund(DRW and Squarepoint) in 2025.

The master’s program is strong overall, providing solid knowledge across the most relevant topics in Financial Engineering. However, there are a few areas for improvement. I would suggest removing some mandatory courses to give students more flexibility to tailor their academic journey to their individual interests.

In general, the professors and TAs explain concepts clearly and often provide valuable insights and suggestions to help you explore topics in greater depth.

Finally, it would be very helpful to organize dedicated sessions to strengthen the specific skills required for quantitative interviews.

I first discovered this course while researching the prerequisites for top-tier Master of Financial Engineering (MFE) programs, particularly Baruch's. I saw it was consistently listed as the recommended and preferred way to satisfy the C++ proficiency requirement, which immediately signaled its quality and relevance to the industry.

My reason for taking the course was twofold: not only to fulfill this key prerequisite for my future MFE applications, but also to build a truly robust and practical foundation in C++ specifically for quantitative finance. I understood that while Python is common, C++ is the language of choice for performance-critical applications in pricing, trading, and risk management.

My experience was exceptionally positive and, while very challenging, incredibly rewarding. The course is masterfully structured, guiding students logically from C/C++ fundamentals through advanced OOP concepts, the STL, and Boost libraries. The true value, in my opinion, came from the final levels, which directly applied all these concepts to financial engineering problems like Monte Carlo simulations and option pricing. This bridged the gap between abstract theory and real-world application perfectly. The TA support was outstanding—prompt, detailed, and professional—and the forums provided a strong sense of community.

Completing this course has given me a deep sense of accomplishment and a genuine confidence in my C++ abilities that I did not have before. I feel significantly more prepared to tackle the rigorous curriculum of a top MFE program like Baruch's and for the technical demands of a future career in quantitative finance. I highly recommend it to any prospective MFE student.

I really enjoyed the C++ Programming for Financial Engineering course. The material was clear, well structured, and the assignments were both challenging and rewarding. I took this course to build a solid foundation in C++ for quantitative finance, and it definitely helped me achieve that. My TA, Avi Palley, was extremely supportive throughout the course and always available to help with any questions or doubts.

I am part of the 2027 class.

Course Flexibility: 9/10
Courses are highly flexible. Whether you are into a highly quantitative domain or a more traditional finance/economics domain, there are options for you. There aren't too much of strict requirements on what courses one has to do, besides basically 5 core courses. Most people I know in the program take courses outside of finance and absolutely love it. You can basically learn anything you want.

Career support: 10/10
Provide a lot of support for internship/full-time search. Obviously, they won't show you every step of the way what to do, but they will check your resume properly, have a lot of career events and talks, and also have a lot of good connections with the industry.

Fun: 9/10
Lots of free food opportunities at talks/events/and seminars. There are like 2 places where students in the program would go for fun, so it is kind of limited, but you can't ask for much when you're in Princeton. I suggest getting a bike if you're coming here.

Swags: 7/10
It could be better. Companies that come to give talks provide really good swags, though.

Friendships: 10/10
The most important thing anywhere. Everyone here is insanely smart. I know people who have started a hedge fund in the past. Everyone is insanely driven. It's also close to ORFE, which is arguably one of the best in the country, so you learn a lot from people outside of the program as well.

Brand name: 10/10
I did my undergrad in a less prestigious school. Immediately upon coming here, I can see the difference in how people recruit/see me. The network and brand name can take you really far at Princeton (obviously, you still have to work for it, but people will definitely interview you).

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!