Is (Paid) Experience in Data Science a Good Proxy for One’s Aptitude in the Field?

Short answer: no. Long answer: although some experience is positively correlated with aptitude in the field, the relationship between the two is neither linear, nor straight-forward. Let’s delve into this more, examining the lesser known aspects of it.

If someone is in the beginning of their career as a data scientist, chances are that having some experience is much better than no experience at all. The experience in this case involves dealing with practical challenges that are usually not described in data science books or courses, so for the inexperienced data scientist, these can be major liabilities in his work. The experienced data scientist has encountered tricky situations where the models she has built have failed and she has a better chance of avoiding similar situations, or at the very least tackling them efficiently when they occur. Do additional years of experience help a data scientist though in her career? It depends. Unmistakably, that additional experience of working in an organization allows the professional to cultivate his soft skills more and be able to work more effectively in a team. Also, his understanding of how a business works becomes more solid and functional. However, data science aptitude does not necessarily grow as the years of experience accumulate. After all, the field changes so rapidly, so having a few years more experience in it may be irrelevant, as the techniques the more experienced data scientist has mastered, may not be so useful or necessary any more.

Of course there are exceptions. If a data scientist is particularly good, due to talent, education, or some combination of the two, then the additional years of experience are going to translate into a more varied expertise and perhaps the ability to lead a team effectively. The thing is that this kind of person is going to be good even with little or no experience, since the innate talent or general aptitude due to good education are there from the get-go. Naturally these cases are few and may be considered outliers, but they are relevant enough to be valuable as they are the exception that verifies the rule.

So, what would be a good proxy for data science aptitude then, if experience is not a good enough feature to predict this valuable variable? Well, it depends on the situation. If you have an organization that deals with text a lot and requires a data scientist to be part of NLP and NLU projects, then some understanding of the language(s) and/or the ability to create and implement scalable heuristics based on text data would be very valuable. These skills would be a better proxy than having spent a number of years on the field, focusing mainly on recommender systems, for example. If an organization wants someone to work on image data and solve challenging problems related to that (e.g. object identification), then a solid understanding of image data or of deep learning techniques would be a pretty good proxy of aptitude related to this task.

Work experience has remained relevant because of its applicability in various professions. However, making the inference that just because it works well with them it should also work in data science is unscientific and reckless, at best. So perhaps organizations that value experience so much are better off being avoided since it’s doubtful that they have a solid understanding of data science, or the ability to manage this kind of resources effectively (perhaps their managers need to gain some more experience in handling certain human resources, who knows?). After all, just because most organizations can benefit from data scientists, it doesn’t mean that they are data science ready.