How to become a quantitative analyst with Mathematics/Economics background

[Mickeristic]

Greeting,

This is my first post ever in this website so I apologize in advance if there is any rule I am not following.

Here is my background information: I recently graduated from University of Wisconsin with a Bachelor's degree in Mathematics and Economics double major, and I want to become a quantitative analyst within the next 2~3 years. I understand that to become a quant, knowledge in math, computer sciences and stats are necessary. I took CFA Level 1 in December and haven't got the results back yet, but I am very certain I will pass. I will also take level 2 and level 3 in following years to work toward getting my CFA Charterholder. I don't have any internship/work experiences related to my fields and I am currently looking for an internship that involves research and quantitative nature that starts in spring, hopefully in February 2019. However, I have zero knowledge/skill in computer sciences, and I plan to take cs courses on websites such as Coursera or Udemy to boost my resume and prepare for a quant career in the future. Lastly, I am an international student on my OPT and I want to participate in 2021 H1B lottery, so I must find a sponsoring employer that will offer me a quant position before that(which means ideally I will start working as a quant at a sponsorship company in second half of 2020).

Given my background, do you have any advices and tips on how I can succeed in becoming a quant within the allowed time-span? I appreciate any useful and constructive information, as I am sure most of you are well-experienced to educate me with my situation.

 

[mhy]

What kind of positions are you interested in and have you looked at job descriptions/job requirements? Where is your skillset now VS what the positions require? "Quant" is a very generic word and there's no one-size-fits-all answer.

 

[Daniel Duffy]

What kinds of mathematics did you do in the degree program: was it 'hard' maths or more 'accessible' stuff? And what can the maths be applied to?

 

[Yin]

This is an interesting post. I am in an Economics PhD program. Not sure if I stand a chance to get a quantitative risk management job.

I am experienced with R and MATLAB, Markov Chain Monte Carlo, Sequential Monte Carlo. Good at statistics and probability theory, stochastic processes, linear and nonlinear time series, hidden markov models. Know some machine learning algorithms.

Do you think a quantitative risk job is where I should go? Or what are the core skills this line of jobs value most? Thanks a lot for your comments and suggestions.

 

[Yin]

Sorry, Mickeristic and everyone. I was not trying to redirecting the discussion to me. I thought one more economics background example may be helpful to the discussion. I am new to QuantNet, and if I broke some rule, please feel free to let me know.

 

[Mickeristic]

What kind of positions are you interested in and have you looked at job descriptions/job requirements? Where is your skillset now VS what the positions require? "Quant" is a very generic word and there's no one-size-fits-all answer.

Yes I understand that I didn't fully elaborate the type of quant position I want to get into. I am currently more interested in the front desk quant/trader position, as I figure the other type of quant positions would require more advanced skills and experiences. To be honest, my skillset for now is very limited. As I said I have zero knowledge in computer sciences, and I plan to enroll in a Python course on Coursera as soon as possible. I will also learn C++ and R, and others later as necessary. Since I majored in mathematics, I had one semester of probability, one semester of linear algebra/differential equations, and all three semesters of calculus. I am planning to learn as much skills as possible required for a front desk quant, during next two years of my career, even while I will be full time working.

 

[Mickeristic]

What kinds of mathematics did you do in the degree program: was it 'hard' maths or more 'accessible' stuff? And what can the maths be applied to?

Hi, I think my most relevant math courses taken throughout my undergrad studies would be three semesters of calculus, one semester of linear algebra/differential equation, one semester of probability. I understand these are no where near enough to be sufficient for becoming a quant, so I will take various courses on websites such as Coursera, Udemy to supplement as much knowledge as possible within the next 2 years.

 

[Mickeristic]

This is an interesting post. I am in an Economics PhD program. Not sure if I stand a chance to get a quantitative risk management job.

I am experienced with R and MATLAB, Markov Chain Monte Carlo, Sequential Monte Carlo. Good at statistics and probability theory, stochastic processes, linear and nonlinear time series, hidden markov models. Know some machine learning algorithms.

Do you think a quantitative risk job is where I should go? Or what are the core skills this line of jobs value most? Thanks a lot for your comments and suggestions.

Hi, I am no way an expert on this but I think you are more than prepared for a quantitative risk position. I mean your PhD title alone is beyong impresssive already. Given the skillset you are good with, I think you will do well in quantitative risk analysis job. But definitely look up other knowledge/skills required for this position, there should be detailed job description outling what skills they are looking for. And don't worry about redirecting the discussion, we can all learn something with it.

 

[Daniel Duffy]

Hi, I think my most relevant math courses taken throughout my undergrad studies would be three semesters of calculus, one semester of linear algebra/differential equation, one semester of probability. I understand these are no where near enough to be sufficient for becoming a quant, so I will take various courses on websites such as Coursera, Udemy to supplement as much knowledge as possible within the next 2 years.

These are essential topics of course. They tend to teach concepts and passive learning (what I tend to see with Economics students) in a sense. More real, applied and numerical analysis is needed IMO that focuses on dong maths and less looking at formulae.

And learn C++.

And developing problem-solving skills are vital as discussed in my "Preamble"

https://www.datasim.nl/application/files/3815/3053/6380/DL_Pure_Mathematics_Foundations2.pdf

https://www.datasim.nl/application/files/4715/2597/1908/Course_Applied_Ordinary_and_Partial_Differential_Equations.pdf

 

[Daniel Duffy]

Mathematics is not a deductive science -- that's a cliche. When you try to prove a theorem, you don't just list the hypotheses, and then start to reason. What you do is trial and error, experimentation, guesswork. The only way to learn mathematics is to do mathematics. That tenet is the foundation of the do-it-yourself, Socratic, or Texas method.

Paul Halmos

my 2 cents