The question was posted on Quora as "What do you look for when hiring an entry-level data scientist? Would a master’s in data science or a bootcamp be beneficial?" The answer below is from Eduardo Arino de la Rubia, Chief Data Scientist at Domino Data Lab.
I think that realistically, either one of them is enough to get me to look at a resume, but absolutely positively neither of them is enough to motivate me to make a hiring decision. I believe there is a misconception for what it is that hiring managers are looking for in entry level data scientists, and the atmosphere of credentialism does a great disservice to people interested in making the transition into a data science career. What I look for the most is some signal that the junior data scientist:
Let’s put aside the need for some level of formal training, that is a non-negotiable baseline. You have to have enough understanding of mathematics and statistics to know when you are getting yourself into trouble, you have to understand data management practice enough to understand how to access data, and you have to understand enough about machine Learning to make the appropriate series of tradeoffs in model development and validation. That is table stakes, however what makes one candidate stand out above the others is everything else surrounding these core concepts.
Drive and Determination to be a Self Directed Learner
Learning in class is easier than learning on your own. You have a professor who is ostensibly payed to teach you a corpus of agreed upon material, they do so under a schedule which is dictated by a syllabus, and they evaluate it using something approaching best practices. Having a stellar academic record which shows you succeed in traditional pedagogy is great, but insufficient. I need to see that you have learned and applied things outside of the traditional canon. If you come from a traditional statistics program, I want to see that you have branched out into some non-statistical approaches. If you come from a background in operations research, I want to see that you have completed some project leveraging NLP, etc… I wholly believe that in data science one of the keys to success is the ability to understand when your efforts will be “leveled up” by knowing when it is time to sharpen the blade. No one tells you that six months from now, understanding how not to overfit a GBM will be relevant, you have to have an intuition and a desire to understand it on your own. This is often showcased by giving talks at local meet ups, blog posts, and GitHub projects. I don’t require world-class scholarship, by any stretch of the imagination, but I do require signal that you understand that the education you were given is an arbitrarily chosen curriculum, and that the set of things outside of that curriculum is worth your scholarship.
I have had the good fortune of being a CS lab assistant a number of times in my life, teaching people the basics of programming. I have also mentored a number of students through introductory MOOCs for programming. An unpopular position I hold is that not everyone is capable of mustering the time/energy/interest/frustration tolerance/luck to be able to learn enough code to build things. I am not making a statement about intelligence or natural ability, I simply am arguing that learning to program is an awful journey that we have not learned to teach well yet, and that unfortunately a significant percentage of individuals who set out on the journey do not make it. Junior data scientists are squarely in the danger zone regarding this particular skill. Very few academic programs or bootcamps have enough time in the curriculums to devote the sufficient energy to writing code.
