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
- chances of getting into PhD on Machine Learning in a top/moderate US/Europe Grad School
- 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