C++ Programming for Financial Engineering
Highly recommended by thousands of MFE students. Covers essential C++ topics with applications to financial engineering. Learn more Join!

Python for Finance with Intro to Data Science
Gain practical understanding of Python to read, understand, and write professional Python code for your first day on the job. Learn more Join!

An Intuition-Based Options Primer for FE
Ideal for entry level positions interviews and graduate studies, specializing in options trading arbitrage and options valuation models. Learn more Join!

Reply to thread

Message: <blockquote data-quote="Peter Wagner" data-source="post: 133518" data-attributes="member: 25130">[ATTACH=full]8252[/ATTACH]Since writing for this blog in January about the HFT/algo job market, I’ve received many inquiries from students asking about the “requirements” for quant jobs on Wall Street.  “Do I need a PhD?” is a frequent question.  Each time I receive one of these inquiries, I struggle with the answer.  My instinct is no.  But when I look at who is working in these jobs, I do see a predominance of PhD’s in the top positions.  PhD’s in mathematics, physics, operations research, EE, etc. are common in the quant community.  So it’s tempting to tell students that a PhD is helpful, but it feels like the wrong answer.  In my gut I know that the people getting these jobs are not getting offers because they have extra letters after their name.  The people in these positions are there because they have proved over their academic and professional lives that they are:List 1<ul> <li data-xf-list-type="ul">Very smart</li> <li data-xf-list-type="ul">Quantitative thinkers</li> <li data-xf-list-type="ul">Good at figuring things out with minimal guidance</li> <li data-xf-list-type="ul">Dedicated</li> </ul>But the above is a generic list of attributes for hiring into just about any job.  So what is it that makes someone hirable as a quant?  The list isn’t long:List 2<ul> <li data-xf-list-type="ul">Education in advanced math (stochastic calculus, statistics, probability, etc.)</li> <li data-xf-list-type="ul">Good software development skills</li> <li data-xf-list-type="ul">Good data analysis skills</li> </ul>Okay, now combine the two lists, and you have the list of qualifications for a quant.So, back to the question of whether to get a PhD.  Should I get a PhD?, asks one student who is angling for a career in quantitative finance.  Will it help me?  Is it necessary?  No, it’s definitely not necessary.  Will it help?  Empirically, it seems to help.  But does it?  I’ve finally come to clarity on the subject with the help of a conversation today with the director of a quant group supporting credit trading for a major investment bank.  Of the two lists above, the important qualifications are on the first list.  This list has nothing to do with your education.  Your success in any field depends on the first list. The 2nd list consists of skills, skills that can come from your education or experience.  They are enabling skills, but they are not dictators of success.  All career success comes from differentiating oneself with respect to the elements on the first list. You can get a PhD, spend the money and the time, but if you don’t differentiate yourself in the fundamental elements of success, the PhD won’t help.So why are there so many PhD’s in quantitative roles, anyway?  I think the answer is pretty obvious.  Very smart people with quantitative instincts are drawn to the PhD path.  Later they find that they are well suited to a career in finance.  They satisfy both lists and hence are successful in quantitative roles in finance.  Almost without exception, these are individuals who pursued a PhD based on their interests and passions (EE, Physics, Applied Math, etc.), not people who pursued a PhD as a means to a job in finance.  QED:  A PhD is not a requirement for a career as a quant in finance.The MFEI feel this article isn’t complete without addressing the MFE degree.  The MFE provides students with the fundamental skills utilized in quantitative jobs.  If you can afford it, it’s an easy way to satisfy List 2.  However, it’s by no means a ticket to success in quantitative finance.  I’ll explore the MFE further in my next post, “The MFE, Is it a Contra-indicator?”As always, you can reach me at <a href="mailto:peter@affinityny.com">peter@affinityny.com</a>.  LinkedIn profile: <a href="http://www.linkedin.com/in/peterwagner123">www.linkedin.com/in/peterwagner123</a> (I keep a listing of active quant roles here).</blockquote>

Verification