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I am a masters in Computer Science and am interested in pursuing a career in Machine Learning, possibly academic, in the long run.

I have been offered a position related to Financial Modelling at Goldman Sachs that mostly would provide experience in optimization and stochastic methods, which are also required for developing Machine Learning algorithms.

How helpful or detrimental would the given job experience be in

  1. chances of getting into PhD on Machine Learning in a top/moderate US/Europe Grad School
  2. research during PhD

after working few (around 2-3) years in the given job ?

Any examples of similar shifts, possibly with ensuing results, would be extremely helpful

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    $\begingroup$ (1) Acquiring more knowledge and experience is only going to improve your chances of getting into a PhD program. That said, a finance gig is not gonna be your most efficient path to a PhD program. The most efficient way into a PhD program is to demonstrate an ability to do original research, by publishing papers or getting great recc letters from advisors . (2) Again it could only help but unless your finance job is to do original research then I wouldn't expect any dramatic benefit in this regard, so I wouldn't give this factor much weight in your decision process, if i were you. $\endgroup$ – jerad Mar 9 '13 at 22:59
  • $\begingroup$ Thanks @jerad ,in that case, it would be possible to pursue independent original research during job, and if the technical knowledge acquired pertains to allied fields, it may be helpful. Also, I was wondering whether spending few years would hinder the chances for PhD considering higher age while later application vis-a-vis fresh undergrad applicants $\endgroup$ – user21760 Apr 12 '13 at 6:01

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