Latest reviews

Background
International student, engineering undergrad with ~2 years work experience. Coming in, I was not from a traditional econ/math/stat/cs background, so I had to actively build up my foundation in probability, statistics, coding, and market intuition over the course of the program.


Courses & Academic Rigor
Overall, the academic side is about as rigorous as I expected from UChicago. If you choose your courses well, you can get a genuinely technical experience, heavy on probability, statistics, machine learning, and numerical methods.

The core sequence that most students take (Stochastic Calculus, Option Pricing, Portfolio, Python) is generally well-designed and closely aligned with common quant interview topics, which definitely helps during recruiting. These courses gave me a solid baseline when it came to interview preparation.

The big positive, in my view, is the flexibility: you can push into more advanced material by taking Stats, CS, Math, or Booth courses. For example, I focused more heavily on Statistics electives (time series, ML, optimization, etc.). For someone with a decent foundation who wants to stretch technically, this is where the program really shines.

That said, there is noticeable variance in technical depth across courses. Some programming or ML classes are intentionally accessible to students with weaker backgrounds, which can feel too basic if you already have serious CS or Stats training. In those cases, you need to be proactive:
- look up syllabi early,
- shop around in the first weeks,
- don’t be afraid to drop a “safe” course for something more demanding.

Hence, the program can be extremely rigorous, but that rigor is not automatically imposed on you. It’s very much a “you get out what you optimize for” situation.


Cohort & Culture
The cohort is large and diverse in background. You’ll meet people who are genuinely very strong technically, alongside others who are still building confidence in areas like coding or probability. From a quantitative standpoint, this variance is both a strength and a structural trade-off. In some courses, pacing naturally targets the middle, which can feel slower if you’re ahead or more intense if you’re behind.

That said, I’ve had a very positive experience with the cohort culture. People are extremely approachable, collaborative, and willing to share knowledge: whether that’s helping debug code, walking through a math concept, or doing interview prep together. Rather than being competitive in an unhealthy way, the environment strongly encourages mutual support.

What makes this especially valuable is the opportunity to leverage complementary strengths. Some classmates are very strong mathematically, others excel in implementation, and others bring market intuition or prior industry experience. When you actively engage with your peers, socially and academically, you benefit from this collective depth. In my experience, the program is at its best when you treat the cohort not as a uniform group, but as a network of people with different strengths you can learn from and grow alongside.


Career Services & Job Market
The dedicated career office and admin team are genuinely engaged. They run many events (some more useful than others) and are responsive when you reach out. They are especially helpful with alumni connections, recruiter outreach, and internship/full-time negotiation questions.

However, it’s important to be clear: they do not and cannot place you into front-office quant roles. For competitive QR/QT/QD positions, outcomes are driven mainly by technical depth, problem-solving ability under pressure, and the amount of independent prep you put in outside of class.

Project Labs are useful, particularly for gaining your first “real” line on the resume and having concrete experiences to discuss in interviews. But these are inputs, not guarantees. Expect to send 100-200+ applications and deal with many rejections, even with a strong profile.


Additional Notes
- The quarter system + recruiting means the first quarter is brutal. Treat interviews and job applications as an extra heavy course from day one.
- Don’t assume every quant-sounding elective is advanced; read syllabi and talk to seniors
- Networking helps, but for quant roles, technical competence is non-negotiable.


Overall
I’m glad I chose FinMath. It gave me the structure, brand, and environment to build on my engineering background and break into quant roles. It definitely rewards students who are self-driven and care about technical depth.

I would recommend the program to students who are ready to treat it as a toolbox and not a guarantee.

Review of the Chicago Booth Master in Finance (MiF) Program — Current Student (Class of 2026)
This program is very new—the first cohort will only graduate in December 2025. I am part of the second cohort, Class of 2026. Booth currently has two Specialized Master Programs (collectively called SMP): the Master in Finance (MiF) and the Master in Management (MiM). Each program enrolls roughly 120–140 students per year.
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Program Structure & Academics
The program begins in late August (around 8/25). Before the formal quarter starts, we have a two-week Boothcamp, which includes an introduction to Python and various school-organized activities, speaker sessions, and networking events. Similar events continue throughout the academic year.
The official academic year starts in late September. The entire month of September is dedicated to an extremely intensive Financial Analytics course (three classes per week for three weeks, nine sessions total). This course is one of the four required MiF courses.
Two other core courses—Accounting and Investments—are taken in the Autumn Quarter, and the final core course, Corporation Finance, is taken in the Winter Quarter. After September, the MiF follows the standard University of Chicago quarterly system.
Student Backgrounds
The cohort is very diverse. Most students come from backgrounds in:
• Finance
• Accounting
• Economics
• Computer science
• Other quantitative or business-related majors
The MiF has a noticeably higher proportion of Chinese students compared to the MiM. The two SMP programs have different core requirements and different program lengths—the MiF is 1.5 years, while the MiM is 1 year.
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Course Selection Flexibility
One major strength of the MiF is its extremely high degree of course flexibility. According to students from the first cohort, MiF students initially had the same bidding priority as MBA students, which created some complaints from MBA students. Course enrollment at Booth uses a bidding/auction system, which feels very much like a top business school experience.
In the first bidding phase, MiF students can bid for courses designated for their program. In later rounds, any MBA or other Booth courses that still have open seats become available. By graduation, students can complete one of three optional tracks based on their coursework:
• FinTech
• Investment Banking
• Asset Management
Courses range widely in difficulty—from MBA-level to PhD-level—and cover an extremely broad set of topics including Advanced Valuation, Venture Capital, Asset Pricing, Data Analytics, ML, and many more. Compared with other schools, Booth offers far more elective freedom. It can be very quantitative if choose to.
Most MiF students spend a significant amount of time at the downtown Gleacher Center in the first several quarters. Later quarters depend entirely on your elective choices.
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Career Services
Booth’s career services are run by the central Booth career development office. MiF students have access to the same online portal as other Booth students, where we can:
• Schedule appointments with career coaches
• Receive resume feedback
• Attend weekly events posted on the platform (speaker series, MBA networking events, company visits, etc.)
Because the MiF program is still new, it is too early to make absolute claims about long-term career outcomes. We will need a few more years and several graduating cohorts before career placement statistics stabilize.
That said, from what I have seen and heard so far:
• A significant portion of the Class of 2025 accepted Sales & Trading roles in Hong Kong (I know 3 Citi acceptant)
• The cohort’s employment destinations seem to split roughly into thirds:
Hong Kong, Mainland China, and the United States
But of course, the official data from Booth—when published—should be considered definitive.
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Admissions & Overall Impression
I’ve heard that the admissions cycle for the Class of 2027 has accelerated significantly. Students from my undergraduate institution mentioned that some applicants had already submitted and completed interviews by November. From the school’s perspective, securing top applicants earlier is a smart strategy.
Overall, as a Master in Finance program offered by a top B-School, I believe Booth MiF has met expectations in its first 1.5 year. I have met many interesting and talented classmates, and the professors are extremely distinguished—some students even had a Nobel laureate teach their Asset Pricing course. Career interests among my cohort vary widely, which creates a very dynamic environment.
Personally, I’m hoping to land a strong role after graduation, and I believe the program provides a solid platform for that.

Academics:
I appreciate the flexibility of the core curriculum. During the first year on the Ithaca campus, students can take courses not only from the ORIE department but also from Dyson and even Ph.D.-level classes. Most courses are practical and applicable. The final semester takes place on the NYC campus with instructors coming from industry. This provides great exposure to real industry though the quality of courses is mixed. I personally have some concerns about the Capstone project, as many projects do not appear to generate meaningful business impact and some sponsors seem relatively disengaged.

Career Services:
Quality of career services have declined since Ayumi left. Resume reviews and office hours are not particularly helpful. Alumni mock interviews, are beneficial, but CFEM seems to lack strong and consistent engagement with its alumni network. Resume drops can help secure some interviews, but landing an internship or full-time role ultimately depends heavily on individual effort and some luck.

Social:
CFEM is a relatively small program with about 60–70 students per cohort. As a result, most students know one another and makes it easy to form friendships.

I took this course with the intention to pivot my career towards algo trading.
The course helped me understand objects and templates a lot better (coming from self-taught python knowledge).

It also helped me answer interview questions about C++ and coding in general.

The course was really comprehensive and really deepened my understanding of C++ applications in financial engineering. I found out about this course through LinkedIn, where I really wanted to learn about Quants and Quant Research.

I didn’t grow up excelling in mathematics, nor did I come from a top-tier academic background. I went to a regular high school with a low GPA. I went to a regular college with no aspirations to continue my education. When I applied to graduate school, I applied to three programs. Two of them rejected me, and by the time I checked my Tandon portal, the acceptance deadline had already passed. I emailed anyway, hoping for the best, and somehow ended up here. It was surreal to realize I had been given a chance at something I had never experienced before: a truly rigorous, prestigious academic environment. This program has changed my life, and while I am deeply grateful, this is no advertisement. I want to give an honest and objective review of my experience. I will discuss the academic quality, the career landscape, and the student body.

When I entered the program, my understanding of finance and probability was minimal. Concepts like long and short positions, sigma-algebras, or even the structure of a stochastic process were foreign to me. By the time I finished the program, those gaps had been filled. Still, the academic experience here is a mixed bag. To begin with the weaknesses: the course structure can be inconsistent. Sections of the same class may cover different amounts of material depending on the instructor, and sometimes the pacing or organization can make it difficult to build understanding from first principles. Compared to the classes I took at Courant, where everything is rooted mathematically before being built upward, some of the MSMF courses focus more on application than on foundational derivations. Depending on your learning style, this can either be refreshing or frustrating. I also experienced situations where my class lagged behind other sections simply due to differences in instruction. This variation makes the academic experience heavily dependent on who you take a course with.

Despite this, the program has a major academic strength that I haven’t seen replicated elsewhere: advisement and accessibility. The faculty—whether full-time professors or PhD students—are genuinely willing to talk, help, and guide you if you put yourself out there. This is where the bulk of my growth happened. I spent hours asking questions, reviewing material, and learning how mathematicians actually think. The environment rewards curiosity. While the classes themselves vary, the people behind them are extremely strong academically and overwhelmingly supportive. This includes the leadership of the program, which is world-class in terms of research pedigree. If you want to learn, there are people here who will teach you. However, I assume you are reading this because you want ROI on your investment in a masters degree, not to enter a PhD program. And to get ROI, you need a job.

Career services are more difficult to evaluate. They try, and some students really do benefit directly from the structure. For me, it wasn’t the decisive factor in landing interviews or offers. The program is large, and naturally not everyone can be individually shepherded through recruiting. I found the alumni mock interviews extremely valuable, but beyond that, most of my progress came from my own preparation, networking, and project work. That said, the NYU name carries weight. Depending on who you talk to, some people associate Tandon with the Brooklyn campus, new and unfamiliar, or simply a solid program. Others see “NYU” and consider it an elite academic background. Either way, the degree absolutely got my foot in the door for enough opportunities that I was able to build a path in quant. It will open doors—what you do once they open is up to you.

The student body is large and diverse, both culturally and academically. Since this was my first time in a truly competitive academic environment, I don’t have much to compare it to, but I can say this: you will meet all types of people. Students who are frighteningly smart, students who work unbelievably hard, students who balance academics with real lives, and students who are still figuring things out. It feels like a real cross-section of the world, which I personally enjoyed. The size of the cohort can sometimes make things feel impersonal, but in other ways it creates a sense of scale—you learn to advocate for yourself, to seek out the right classmates, and to build your own circle.

To conclude, my experience at Tandon has been transformative. The academic quality is strong, though sometimes uneven; the career services help some more than others; and the student body is what you would expect from a large, diverse, competitive graduate program. What makes this place special, in my opinion, is the combination of accessible faculty, rigorous optional pathways, and the freedom to shape your own trajectory. This program won’t carry you, but it will give you every opportunity to succeed if you’re willing to put in the work. I came here with gaps in my background, and I left with the tools and confidence to build a career in quant. For me, that alone made the experience worth it.

Disclaimer: I advocate for a large student body, if the program was more selective I may not have gotten accepted. I consider myself as someone who has found success in this program, and I hope others may find similar experiences to mine. My review sits at 4 starts because there is room for improvement, but this program has changed my life.

Quick shoutout to Courant: the students and professors there are awesome! I hope there is more integration between our programs down the line. Much love

Cannot recommend this program enough. After having been briefly exposed to other programs, the sponsored research projects sets Lehigh apart from even the top programs in these rankings. The career placement is very good, especially if you show yourself to be driven and willing to put lots of work in.

I’m currently a student in the Lehigh MFE program, and my overall experience has been positive, especially in terms of academics and faculty support.The curriculum goes deep into probability, stochastic processes, optimization, risk modeling, and financial mathematics.
The professors are the biggest strength here: extremely supportive, accessible, and genuinely invested in student success.

The MS in Financial Engineering program at Lehigh University is a demanding, well-designed curriculum that blends finance, mathematics, and computational tools to prepare students for real-world careers in quantitative finance, risk management, and fintech. The coursework—ranging from stochastic processes and derivatives pricing to machine learning and fixed income provides both a solid theoretical base and strong practical skills.

Lehigh’s faculty, industry partnerships, career services, and project-based learning create an environment where students gain meaningful insights, build valuable networks, and strengthen their career prospects. The program also offers excellent computational resources and a collaborative culture that supports deeper learning. In addition, the program manager plays an active role in guiding students through the job search process.

With its academic rigor, industry relevance, and strong career preparation, I confidently recommend Lehigh’s MSFE program to anyone aiming for a successful future in quantitative finance.

I joined with an impression that it will be mostly machine learning and alpha research. I am partially correct, because while both are taught, I also get to study stochastic calculus and derivative pricing as well as risk management.

The program focuses on bringing the more well-used side of these theory into python code. While I did not get to study the more esoteric equations of stochastic calculus, we get to implement the modelling of volatility surface using tick data of deribit.

The students are a hit or a miss. This is because at a base level, those that can make it in a Quant field is already low. One of the first task you will want to do is to make sure you are a reliable group mate, and the task after is to find other reliable group mate.

I also heard that the cohorts after mine benefits from getting a credit bearing internship too.

By itself, it is unlikely to be enough to push most of the graduates into a Quant role. Graduates will have to make sure they stand out amongst the other target school. It gave me an edge to be recognised for a Quant Developer role, and the risk and stochastic modelling lecture proves to be useful when my company implements option on their exchange. However, it also took my existing strength as a com sci graduate and work experience to land the job.

All in all, those that are willing to put in real effort, and already have what it takes, will benefit from this program. It could be the final push/foot in the door for Singapore job hunting (although the job market is very bad at the time of writing). Those looking to take this just for the degree, for connection, or without knowing what they want from the program, may only leave with a lighter bank account.

LSE MSc Quantitative Methods for Risk Management Review

Pre-sessional course (MA400):
The programme begins with a two-week pre-sessional course covering probability theory, measure theory, and stochastic processes. The aim is to bring everyone to a common mathematical baseline. Because of this, you do not need a pure maths, statistics, or finance undergraduate degree. In my cohort there were students from economics, finance, business, and accounting backgrounds. The degree becomes mathematically heavy, especially in Stochastic Processes, so MA400 is important. It is fast paced, but the assessment is not counted towards the degree, and the material reappears throughout the degree. The module content also felt relatively self-contained, so anything assessed was covered in the module itself. The only exceptions are some calculus or matrix algebra that would be assumed knowledge. In this case there are usually resources on the module page to cover the prerequisites.

Curriculum and module choice:
There is a wide range of modules and some flexibility to choose courses that are not formally listed as programme options.
Compulsory modules when I studied were:
• Stochastic Processes (ST409)
• Statistical Methods for Risk Management (ST429)
• Computational Methods in Finance (MA417)
I understand that MA417 is now being replaced by Stochastic Simulation, Training, and Calibration (ST463). Although the degree is part of the Statistics Department, students can take modules across Mathematics and Finance. This provides significant freedom if you want more coding-based modules, more probability theory, more machine learning, or more finance.

Workload and assessment:
The degree is academically intensive. Assessment style varies by module, but expectations are always made clear. Weekly problem sheets are standard. Tutorials go through selected problems, but there is not enough time to cover everything, so it helps to work through the sheets beforehand and bring specific questions.
Exam preparation is generally straightforward. Some modules are relatively new or have updated content, so problem sheets are often the most relevant preparation material.

Teaching quality:
Lecturers are experts in their fields, approachable, and willing to answer detailed questions. Office hours are easy to access and there are forums for each module to ask questions (this rarely got used, however). Tutorials have mandatory attendance and will either be taught by the lecturer or a PhD student, but they are the best place to ask for specific advice on working through problems. I never had an issue with PhD students not being equipped to teach, in fact one of my favourite tutorials was run by the PhD student.

Student experience:
The cohort is mostly international and highly motivated, so the academic environment is competitive and hard working. LSE offers many societies and extracurricular activities. At the start of the year there will be an event showcasing the various societies, including sports, academic, and hobby societies.
A useful extra is the free language course scheme, with options such as Japanese, Chinese, French, Spanish, German, and Arabic.

Career support:
LSE Careers can help with interview practice, CV reviews, and cover letter feedback. Careers support continues for many years after graduation. Being in London is a major advantage because there are frequent company presentations and networking events. Lecturers often have industry connections, and we had several guest speakers from quant and risk roles joining lectures.

Alumni network:
LSE has an extensive alumni network and a dedicated alumni centre on campus. On LinkedIn it is common to find LSE alumni at most firms you might be interested in. This makes informational outreach and networking easier.

Job outcomes:
An LSE masters is highly regarded by employers. Graduates typically pursue roles in risk management, quantitative finance, as well as data-focused roles in finance. Being based in London gives strong exposure to firms and increases access to events and hiring pipelines.

My experience:
I didn’t get involved in much extracurricular, but I did do the Jane Street Estimathon, which was group tasks for various Fermi estimates, and the LSE Statistics Practitioners’ Challenge which involved analysing insurance data and using various machine learning methods to produce a report and presentation. My modules were,

Term 1:
• Stochastic Processes (ST409)
• Statistical Methods for Risk Management (ST429)
• Computational Methods in Finance (MA417)
• Quantitative Methods for Finance and Risk Analysis (FM442)

Term 2:
• Reinforcement Learning (ST455)
• Bayesian Machine Learning (ST451)
• Stochastic Simulation, Training, and Calibration (ST463)
• Advanced Time Series Analysis (ST418)

When I took the degree, MA417 was compulsory in Term 1 and ST463 was offered as an optional module in Term 2. There is no dissertation or thesis requirement. Assessment is based entirely on exams and coursework. My two favourite modules were Stochastic Processes and Reinforcement Learning.

There are plenty of pros to this program:
• Location
• Teaching quality
• Networking opportunities
• Strong recognition in quant, risk, and finance recruitment
• Access to resources like Bloomberg Terminals
And in my experience the only major downsides are the cost of living in London and the fact that programming was only done in Python and R. For my module choices, I was not exposed to any C++, which is typically used in high frequency and algorithmic trading.

The coursework and professors are as good as you will get at any other university. What really sets Lehigh apart is the project work that students take on. Students take on 2-4 projects, which are sponsored by companies like Point72 and Hamilton Lane, that give them practical experience to add to their resume, discuss with professionals, use in interviews, and develop as quants. The program is growing to include more sponsors and opportunities year over year. The Lehigh alumni community is well established and the MFE network is growing every year to boost future classes.

I think this program is probably the best MFE you can find in the UK. I highly recommend it, especially for students who are certain about pursuing career in quant finance.

In the UK, there aren’t many financial engineering programs to choose like the US. Some people compare this one with Oxford. But aside from the title, I genuinely think this program is more worthwhile. It’s slightly longer in duration, offers more electives, and the timeline is more in sync with industry recruitment.

The curriculum covers nearly everything you would expect for quant. It still focuses primarily on sell-side pricing, lack of ML or deep data science, but still includes those basic ideas. One of my favorite courses was Market Microstructure, which was excellently taught.

The workload is intense, especially in the first term. If you don’t have a strong background in math or CS, it can feel quite overwhelming. There’s a lot of material packed into a short time. But that also means you learn fast and grow quickly, and it’s very rewarding if you’re committed.

On the career service side, every week in the first term there are two career events, practitioners and company presentations. The program is well-known in the UK industry. Of course, getting a quant offer still depends on your own preparation and skills, but this program definitely helps you get closer.

You’ll also meet a lot of like-minded students, many of whom are serious about the field and willing to push themselves. The atmosphere is intense but also motivating.

There’s one thing I would suggest to improve is the timing. The program starts quite late, around the end of September. By then, some have already started applications, the long summer feels a bit wasted.

Review for QF program at SBU.
For context, i have computer science background with some fundamentals in engineering and mathematics, machine learning, linear algebra, probability theory. I wanted to pursue the field of quantitative finance and work as a developer in the field. Hence started to pursue masters in financial engineering at first and then transferred to SBU after a semester for having a chance to pursue phd in the field.

Review of Masters program.
1. Though NYU has a financial engineering program, it felt as if only few courses are aimed for understanding the field, which can mostly be learned from hull book, i liked the financial computing course in c++ apart from that we have random courses and electives like news analytics as well. Its more targeted for understanding financial products, than mathematical analysis they would only teach exisiting proven knowledge.

At SBU the course structure is vastly theoretically both core courses and electives in the field of applied mathematics and qf related courses, like even in risk management course, we were thought theoretical basics of time series analysis and data, derivations etc. i liked stochastic modelling course and operations research, probability theory course, we have measure theory and sigma algebra taught for advanced prob courses. Linear algebra for computational methods and optimization. Ofc given all this ml courses were good as well, also statistical learning. It was amazing learning experience.

2. For job search i am sure nyu has more options and better reach out and interview prep, referrals than sbu. Sbu has good courses and projects but less focus on career opportunities. But because i had cs background it was easier for me to focus on general problem solving and leetcode. I cant say the same for my cohort at sbu most are non cs engineering (mechanical, civil etc) or finance background. But for someone who knows what they are doing it should be fine for interviews and its mostly role dependent. But nyu grads are more targeted for risk management roles SBU grads not so much its more diverse.

3. The faculty at sbu is amazing, robert frey, andrew mullhaupt they are genius with real time experience. Eugene finberg he is probability theory expert. Many others, i am sure NYU also had good faculty but i like the depth of sbu faculty better for qf related courses.

4. My biggest complain at sbu is admissions dept,i am honestly not impressed with the cohort overall. There is a gap between the faculty and people who admit students. I am not sure if its changed now. But previously i see some students, who have no clue why they are in this area of study. Like some have finance exp, some are “ml” experts but clearly its quite basic. Although i have seen some extraordinary people also especially in phd dept, a better cohort always helps.

5. School culture, the dept together can benefit for more events centered around some clubs and projects or competitions. It can be good networking opportunities, i know they are doing alumini connects and do have good stuff it can do better like some poker tournaments or hackathons.

6. Overall can’t complain much, end of the day its all about how serious can you prepare and crack the interviews no matter how good the school is, but i felt there should be more people with serious roles in industry.

Thanks

Putting disclaimer upfront, I wouldn’t comment on specific courses or professors, partly because I graduated a decade ago and things changed or escaped me, but more importantly casting lights into corners isn’t that helpful when the real need is an accurate picture of the overall landscape.

Sure. One can complain about the faculty, curriculum, career service, or tuition. But you would probably heard similar things if you talk to students of most other programs. And the almost perfect programs are just too selective to be broadly relevant.

The real question comes to understanding the programs, picking the fit one, and leveraging it to the best. The same program always have successful graduates every year, and can never ensure success for everyone, actually for anyone. It’s just a car, with its pros, cons, and price. We are the drivers, responsible for maneuver and navigation. Therefore why don’t focus on picking the best courses/professors and let the dean worry about the worst?

Looking back, I’m grateful for its quantitative rigor, diverse curriculum, and unparalleled location. Over the ten years, I also witnessed its improvement in candidate caliber, curriculum quality, and career service. I am giving a 4 star rating because it obviously has room to evolve, but the runway it paved is enough for takeoff.

The admissions committee generally does a solid job selecting capable candidates, and the curriculum itself is strong. I genuinely learned a lot here. The only thing I wish was different is the overall competitiveness of the cohort—if the classroom environment had been more consistently strong, this program would have been even better.

The professors are knowledgeable and experienced, but this is not a program where you can sit back and expect everything to be handed to you. You need to ask the right questions and actively participate. Many undergrads in Stevens QF are extremely sharp, so interacting with them can help you understand where you personally need to improve.

A big warning: people who come expecting an “easy U.S. master’s” get overwhelmed very quickly. The content is math-heavy, coding-heavy, and dense. A lot of students end up relying on AI tools for assignments and projects. Don’t do that. Use AI only as an assistant (debugging, explanations, intuition), not as a solution generator. You’ll be cheating yourself in the long run.

One thing I feel strongly about: self-study is the only way to truly extract the value and ROI from this degree. If you’re a complete beginner in math, coding, or quant finance, this program will feel extremely intense unless you put in serious work outside of class.

There are also some structural limitations. A single 2.5-hour class per week makes it hard for professors to cover everything in depth. Many lectures feel compressed, and difficult topics get skimmed over because of time constraints. I really wish the program had:

* Recitation classes like in undergrad
* More problem sheets or practice sets
* Two classes per week per course instead of one
* More structured quant interview prep
* More consistency in course difficulty across sections and instructors

Another reality: *18 months is not enough* to deeply cover all the math, coding, stochastic calculus, derivatives pricing, and quant interview theory required in the industry. So don’t be afraid to ask professors if something feels unclear. Whether you lack the background or the topic moved too fast, they will help you or share resources. And if it still isn’t enough, the TAs are usually very supportive.

On the career side, it’s important to set expectations clearly. **More companies open roles for Stevens undergrads than for graduate students**, especially in quant and technical roles. Career services are, honestly, below average. They provide basic support, but you cannot rely on them for meaningful recruiting outcomes. Networking, alumni outreach, and building strong projects matter far more.

The overall student quality is mixed—some exceptional people, but many students are underprepared or lack the motivation expected at a quant-focused graduate program.

Overall, Stevens MFE is a good program if you are self-driven. If you rely only on classes, expect professors to spoon-feed content, or hope career services will secure your job, you might struggle. But if you proactively study, ask questions, build projects, and push yourself, the program can absolutely deliver strong value and prepare you for quant, QR, QD, or data-heavy finance roles.

I’m currently an MFin student at MIT Sloan, and I wanted to share my honest experience for prospective applicants.

What I like about the program:

Unmatched Academic Rigor and Flexibility: Cohort is full of smart and hardworking folks. I did not feel the same when I accepted the admit but people are competitive (in a good way), which motivated me a lot. At the start, I felt that the program is more focused on non-quant side and there are very few people who are targeting quant as MIT’s MFin is oe of the few programs that offers you a wide range of concentrations and tracks one can take. But there are many people who I found to be quant focused including the curriculum's deep dive into quantitative finance, machine learning, and analytics.

Access to Research & Industry Practitioners: Professors are world-class and approachable, often bringing in leading industry executives and alumni for seminars, guest lectures, or networking. You can ask any type of question in the class and it will always be encouraged, including the basic ones. Some professors are amazing teachers.

Collaborative, Ambitious Community: The cohort is diverse and intellectually curious, with classmates from elite undergrad and professional backgrounds worldwide. You’ll find plenty of group projects, student clubs (quant finance, fintech, entrepreneurship), and networking events. For me, group projects helped me learn a lot of non-quant things like Accounting, Corporate Finance methods etc.

Room For Improvement:

Career Support: The CDO (career development office) is proactive but you'll have to do most of the things related to internship or full time recruiting. They do offer dedicated resume workshops, mock interviews, and direct connections with top-tier firms in both buy-side and sell-side finance roles tho.

Training Before Program Start: People who come from CS/Math or related background will find it tough to cope up with a couple of courses in the summer as you find many new concepts being thrown at you, and you have to learn it pretty fast. If you have done Eco/Corporate Finance/Quant Finance or similar tracks before, it will come very easy for you to grasp things in class and ace exams with 30 min of study a day before.

Pro:

- You can shape the degree however you want, amazing flexibility starting in the fall term. If you want something closer to a finance-focused MBA, you can do that. The structure lets you build your own path.
- You are surrounded by very impressive people.
- Once the fall term starts, the teaching quality really stands out. Some professors genuinely change the way you think about finance, risk, incentives, and markets. The right professor can turn a class into one of the highlights of the program.
- Being around MBA students and PhD students with more experience gives you a lot of value. You can join clubs, explore different areas, and even take classes outside Sloan. The environment is rich if you want to take advantage of it.
- MIT brand: The name helps. It opens doors and people immediately recognize it.
- Boston is beautiful

Cons:

- Recruiting is self-driven; there is not much guidance. You need to drive your own process, be proactive, and stay on top of timelines. They could help you more. I was a little disappointed by that.
- The summer intro term is heavy and not particularly rewarding. It also overlaps with early recruiting, which makes it feel even more stressful. Thankfully, the real program feels completely different once fall begins.
- High workload

Overall, I am satisfied with the program- I received what I expected from it.

Admission was straightforward: submitting the required documents and a brief written response at some point. Nothing unusual or tricky in the process.

The courses are taught at a generally very strong level, and I agree with previous reviews noting that students really do take a lot from them. My only small criticism is the heavy emphasis on stochastic calculus- officially two core classes devoted to it, whereas I personally believe one would have been enough given how many other courses rely on it indirectly. My favorite classes were Monte Carlo Simulation with Ali Hirsa and Volatility Modeling with Amal Moussa.

Career services were supportive and encouraging during both internship and full-time recruitment. They shared opportunities that are not always visible to everyone and offered useful suggestions. Nevertheless, it ultimately comes down to students to secure positions through interviews. The Columbia brand opens doors, but not all of them.

Classmates formed a strong group- both international and domestic students were with impressive backgrounds. The alumni network can be very helpful if used well.

Overall, I got what I wanted from the program and feel it gave me a solid foundation for my career. I don’t think it is the absolute best program, but there aren’t many that are meaningfully better- perhaps with the exception of two.

King’s College London is a great university to be at, and the Strand campus, where this programme is taught, is a brilliant place to study. Overall, this is a good course in terms of the content: it enables students to develop advanced financial knowledge while further strengthening their mathematical abilities. It teaches you how to apply mathematics to finance, and how to utilise machine learning and programming languages such as Python and C++ to support this. In addition, there are many opportunities, both within the course and through wider university events, to meet and hear from industry professionals.

The course itself is very challenging, and although the structure is quite good, certain improvements could make it significantly better. There are good modules available, but it would be better if there were a wider selection. The dissertation at the end allows students to work in a very practical way, putting much of what they have learnt into practice while continuing to learn more.