Reality Check: Fall 2023 (MFE)

[finders_fee]

Hi,

I'm a 25 year old Master's of Data Science student at the University of Denver. I spent the past summer as a Machine Learning Engineer (Intern) and although I enjoyed the math + statistics, I did not enjoy the software engineering aspect. My professor recommended that I check out becoming a quantitative trader and/or a quantitative researcher. I think that my school name is not strong enough to give myself interviews and I am now interested in pursuing an MFE.

How are my stats for applying for Fall 2023?

United States Citizen

Undergrad:
BA Economics
Indiana University Bloomington (2019)
GPA: 2.7

Graduate School:
MS Data Science
University of Denver (May 2023)
GPA. 3.9

Work Experience:

Two years at Qualtrics (tier two tech company) as a Technology Consultant.

4 months at an early stage startup as a Machine Learning Engineer (Intern)

GRE:

I have not taken the GRE yet and plan to in November. I am under the impression that I must do extremely well on the GRE given my undergrad GPA.

Schools that I will apply to:

UChicago
Princeton
Columbia
CMU
NYU
MIT
Cornell

On my resume, should I list the classes that I've taken in graduate school, my programming projects or all of the technical skills I have from the years I've spent working?

Thank you so much!

 

[Daniel Duffy]

I did not enjoy the software engineering aspect.

May I ask, what did you not like? content, style, teacher?

 

[finders_fee]

I did not enjoy the software engineering aspect.

May I ask, what did you not like? content, style, teacher?

Hi Daniel,

Honestly, I just dislike feature development. The process of designing software ( managing Git, code reviews, pushing to prod etc) was very monotonous to me. I think being a site reliability engineer may be interesting (this is in infra), but regardless, becoming a Quant has peaked my interest at the moment.

 

[WorkofArt]

I'll start by listing down things that you have working for and against you.

Strengths:
Great graduate GPA, good work experience. It shows your good with programming, ride that wave.
Weaknesses:
Undergraduate GPA. Obviously, this can't be taken at face value. Have you taken all the required math courses? (Calculus, Linear Algebra, Probability and Statistics). If yes, what grades did you get in the respective courses?

I looked at your graduate curriculum, I hope you've taken the more quantitatively heavy courses. Advanced Probability and Statistics, Machine Learning, Advanced Calculus and Algorithms for Data Science immediately stand out to me.

Coming to the crux of your question, you don't need to include everything you mentioned in your resume. Most of the schools you've mentioned ask for a list of courses you've done in prior degrees. Some even ask for the textbooks used in the courses to get a better understanding of where you stand with your technical skills.

Include a couple of relevant projects detailing the technical aspects. Same goes for your work experience. What problem did you solve? How did you solve it? For example, if you solved an ML problem, what techniques/algorithms did you use? How did they perform? You don't need to go into too much detail on your resume as you have an opportunity to explain them in your essays. Make sure your resume outlines your experiences appropriately.

Regarding the GRE, it's kind of a grey area. It can't make up for bad grades in your math courses, but a strong GRE would definitely help your case.
If I were you, I would apply to a few lower ranked programs as safety options. That being said, I think you have a fair shot at some of the top 10 programs.

 

[finders_fee]

I'll start by listing down things that you have working for and against you.

Strengths:
Great graduate GPA, good work experience. It shows your good with programming, ride that wave.
Weaknesses:
Undergraduate GPA. Obviously, this can't be taken at face value. Have you taken all the required math courses? (Calculus, Linear Algebra, Probability and Statistics). If yes, what grades did you get in the respective courses?

I looked at your graduate curriculum, I hope you've taken the more quantitatively heavy courses. Advanced Probability and Statistics, Machine Learning, Advanced Calculus and Algorithms for Data Science immediately stand out to me.

Coming to the crux of your question, you don't need to include everything you mentioned in your resume. Most of the schools you've mentioned ask for a list of courses you've done in prior degrees. Some even ask for the textbooks used in the courses to get a better understanding of where you stand with your technical skills.

Include a couple of relevant projects detailing the technical aspects. Same goes for your work experience. What problem did you solve? How did you solve it? For example, if you solved an ML problem, what techniques/algorithms did you use? How did they perform? You don't need to go into too much detail on your resume as you have an opportunity to explain them in your essays. Make sure your resume outlines your experiences appropriately.

Regarding the GRE, it's kind of a grey area. It can't make up for bad grades in your math courses, but a strong GRE would definitely help your case.
If I were you, I would apply to a few lower ranked programs as safety options. That being said, I think you have a fair shot at some of the top 10 programs.

Hi,

Thanks for the reply, this is very helpful.

My undergraduate degree was not quantitative. It only went up to calculus 1, had one statistics course and one econometrics course. My lowest grades were in the qualitative courses. I maintained Bs and As through the quantitative courses.

In graduate school, I have taken Advanced Probability and Statistics, Machine Learning, Advanced Calculus and Algorithms for Data Science. I have received an A in all of these courses.

I've attached my anonymized resume. Feel free to critique it as you see fit.

 

[jarryds]

I think other people on this forum would disagree with me, but IMO it doesn't make sense to have two MS degrees. I would try to apply to some entry level industry jobs/programs and see what you can get. Use this time to fill the knowledge gap by self studying and work on a few quant based projects. You can also reach out to recruiters/industry quants directly to see what they like/don't like about your background. I think their advice would be much better than what you would find on this forum.