[MFE, Stats, DS Master's Applications] Yale | CMU | Oxford | MIT

By sharing my own application journey, I hope to help everyone better understand the admission standards for master's programs in Financial Engineering, Data Science and Statistics, and how to prepare for them!

Video version:

https://youtu.be/_9Nqw-dbpSc

[Application Results]

[Admission]:

Yale - Asset Management

Oxford - Mathematical & Computational Finance

CMU - Computational Finance

[Waitlist]:

MIT - Finance (FE Track)

Yale - Statistics & Data Science

[Rejection]:

Princeton - Finance

Stanford - Management Science & Engineering (FE Track)

Harvard - Data Science

Cambridge - Mathematical Statistics

[Background]

[Education]:

NCKU Stats (Double Major in Econ)

GPA: 4.1 / 4.3

Academic Excellence Award x 4

[Test Scores]:

GRE: 327 (V157 Q170 AWA3.5)

TOEFL: 109 (R29 L29 S25 W26)

[Awards]:

Kaggle Machine Learning Competition, Silver Medal x 1

Hackathon Machine Learning Competition, Honorable Mention x 1

[Research Experience]

Research Assistant @NCKU Nordling Lab

**Anomaly Detection, Bootstrap Resampling, Classification

Publications:

1st author DS research paper (under review)

[Work Experience]

Tech Summer Analyst @Foreign Bank

Quantitative Research Off-Cycle Intern @Hedge Funds

Wealth Management Summer Analyst @Foreign Bank

Quantitative Research Off-Cycle Intern @Prop Trading Firm

Full-Time Quantitative Researcher @Prop Trading Firm

[Recommendation Letters]

NCKU ME Professor x 1 (my research advisor; the lab is in the Mechanical Engineering department)

NCKU Stats Professor x 1

Prop Trading Firm Manager x 1

Hedge Funds Manager x 1

[Choosing Schools]

I'm very sure I'll keep going deeper into quant, and based on what I've learned about the field over the past few years, I believe more advanced knowledge of Stats, Data Science and Financial Engineering will help me reach another level in it. So every master's program I applied to falls within these three fields.

My other reason for studying abroad was that I had always wanted to take classes at a highly reputable university, experience what the world's top higher education looks like, and do research with brilliant professors. So I didn't pick any safety schools before applying; I only applied to the programs I "truly wanted to attend." Many people might think this approach carries a very high risk of getting rejected everywhere, but from my point of view, studying abroad costs both money and time, and I don't want my future self to have even the slightest regret about the choices I make now. Applying to fewer programs also has one big advantage: I had more time to dig into each program's distinctive features and carefully write a different version of the SOP for each school.

[Application Basics]

[GPA]:

If the programs you plan to apply to overlap heavily with mine, I think keeping your GPA at 3.8+/4 is enough. Judging from past admission data at different schools, most applicants' grades fall in this range. That said, remember to look at past admits' backgrounds when you apply: most people admitted to these programs studied Math, CS, Stats, DS, Quant Finance or other related STEM majors in undergrad. Besides showing that schools clearly favor students with these backgrounds, this also means applicants switching fields need to fill in as much of this knowledge as they can during college to make up for their disadvantage.

(Taking more CS, math and statistics courses helps far more than padding your GPA with easy gen eds.)

[GRE/TOEFL Advice]:

From my experience and what I've seen of past admits' backgrounds, these two tests are usually just a threshold and won't be a major factor in whether a school admits someone. I think 325+/105+ is enough to clear schools' hard cutoffs, but if you're applying to some of the elite programs at Princeton or Stanford, I think scoring 330+/110+ is still somewhat safer.

Since the GRE's vocabulary, reading passages and writing are clearly a level harder than the TOEFL's, I think taking the GRE first and then the TOEFL is the easier order. You can even start practicing TOEFL listening and speaking while preparing for the GRE, and taking the TOEFL right after you finish the GRE is also a good option.

GRE scores are valid for 5 years. I suggest starting to prepare around junior year and getting it done during junior year, because most people get buried in countless internships, research or theses in their junior and senior years. If you wait until after graduation to start, you'll be too close to application season, and too much time pressure can cause needless anxiety and make it easier to underperform on test day. So I recommend finishing these standardized tests as soon as you can, at a time when you're relatively less busy.

[Internships]:

Most MFE programs are career-oriented. If students land jobs at big-name IB/Hedge Funds/Prop Trading Firms after finishing the master's, it boosts the program's reputation. So admissions officers want to see in your CV your ability to network and job-hunt during college, or solid substance from full-time work after graduation. That makes finding internships in your junior and senior years very important.

Beyond just getting an internship, I think its content should fit your intended field to some degree (e.g., honestly, I don't think interning in audit or consulting and then applying to an MFE adds much). So I suggest that students planning to apply for an MFE look for internships related to quant, whether Risk Quant, Desk Quant or Quant Strats. Quant internships in Taiwan are extremely hard to find and have a high bar, so I think getting something first and then aiming higher is also a good strategy.

If you really can't find a relevant opening, I think you should at least find an internship related to Finance or Data Science. A big-name company is even better, since admissions officers will trust that the internship was substantial.

** When I first looked for Quant Trading internships, I kept hitting walls. In the end I decided to do Summer Internships at several foreign banks first to get big-name experience, then used that experience to improve my chances of getting into quant. Remember: never leave the summers of your junior and senior years empty!

[Research]:

If you're applying to more scientific fields like Stats or Data Science, a fair amount of research experience helps your application a lot (though from what I've seen, in recent years this is probably standard for every applicant, and you may only stand out a bit with a more formal publication). So I suggest starting to ask professors in your department about research assistant opportunities in your sophomore or junior year.

In my experience, doing a complete research project with a professor not only gives you a deeper understanding of the whole field, but also helps you think about how to apply theoretical methods to solve real-life problems. On top of that, your advisor will usually be willing to write you a strong recommendation letter. So I think seizing the chance to do research in college, learning more complete knowledge and getting a recommendation letter, has a very high return on investment.

[SOP]

Paragraph 1 & 2:

In the first paragraph you can state your motivation for applying directly and clearly, then briefly introduce your background and describe the effort you've put into the field, e.g., which courses you took in college, what kinds of projects you did in them, the subfields you especially like... After that, you can tie these experiences back to what you learned over four years of college and the difficulties you faced along the way to strengthen your motivation.

Paragraph 3:

After the first two paragraphs, I go straight into the knowledge and practical experience I have in the field. In the third paragraph I usually focus on my research experience: briefly cover the research topic and direction, then go right into the details, e.g., which statistical methods you used, each step of data preprocessing, what problems came up and how you solved them... Personally, I think explaining these experiences clearly not only fully shows how mature you are in data analysis and your ability to do independent research, but also makes professors more convinced of your passion for the field.

Paragraph 4:

In the fourth paragraph I focus on my internship experience. Since all my internships involved data analysis, I try to describe some more complex projects to show how I used data analysis to help the company solve problems or improve efficiency. Of course, I also ran into countless practical problems or dilemmas with no solution at the time, and I write about all the difficulties I faced at work too. Besides showing that I really need more advanced knowledge to help me solve these problems in the future, describing them also gradually ties back to the motivation I mentioned at the start.

Paragraph 5 & 6:

After roughly covering my motivation and the abilities and potential I already have, in the last two paragraphs I write in great detail about each school's program and its distinctive features (e.g., MIT Capstone project, Yale Colloquium, etc.), what attracts me to them and how they can help me grow. I also go through the syllabus of each course and say in the essay that I hope to take course XX to improve my YY skills. For research-oriented programs, I look through every professor's research areas and skim a few of their papers, find the professors whose interests are close to mine, and clearly state my determination to learn from them.

[Interview and Test Prep]

[Math and Coding Tests]

If you plan to apply to Princeton MFin and MIT MFin, you'll face a Quant Assessment during the process. I think the math is harder at Princeton > MIT, but overall it's fairly approachable. After all, each subject only has 1-2 questions, and the scope mostly focuses on core concepts, so if you prepare, you don't need to dig into the finer details.

Math test coverage:

1. Probability

2. Statistics

3. Calculus

4. Linear algebra

5. Differential equations

6. Stochastic processes

As for the coding test, only MIT MFin requires one, and it basically covers very simple algorithms. I've heard everyone gets random questions, but I think people who code regularly don't need to spend extra time preparing.

[Interviews]

MIT, Yale and CMU have a Video Interview before/after you submit your application. Overall the format is very similar to MA programs at multinationals or investment bank interviews: mostly random behavioral questions that assess applicants' spoken communication. Yale also requires you to write a short essay within X minutes (5 minutes, if I remember right) after all the interview questions.

Even though you've already submitted your TOEFL speaking score when applying, your Video Interview performance still matters a lot in the process. According to historical data on these programs available online, the review of application materials + the video interview usually cuts about 60-70% of applicants, and only the remaining applicants get a chance to be invited to the next round of live interviews.

My advice for the live interviews is to relax as much as you can and express your thoughts in an organized way. Practice both behavioral and technical questions a lot. Don't exaggerate your CV when you apply, because interviewers sometimes ask in great detail. I think as long as you show enough confidence and do your best to show your passion for the field during the interview, you'll be fine!