How to choose internship in undergrad for a data science career

Jan 8, 2018 | 1620 Views

The answer to this question depends to a great extent on the role/industry/company combination. However, there are few general remarks that can be made.

To start with, it is very important to complete an internship rather than do an "ML related summer research project" unless that research is done with respect to a lab at a university and there is an explicit goal defined at the outset of publishing a paper that will help gain admission to a top graduate program in machine learning. Having said that, while there are many internship roles in data science at tech companies, finding companies that actually hire undergraduates is bit of a task. One needs to be aggressive, at times apply for and follow up with the recruiters on roles in which a graduate degree is "recommended" or even "required". It is tough to find companies that are willing to take chance with younger candidates. One way could be to enter as a Ă¢??data engineerĂ¢?? and work on infrastructure related to the data science team. This can give valuable insights into the day-to-day efforts of a data scientist. 
It is also important to choose to work on a product with which one has some familiarity. This factor often gets underrated. As a data scientist, one would be constantly asked to generate and test hypotheses about the product, suggest future directions & produce insights. If one is an active user of the product, this becomes much easier and in fact fun also. So one should target companies that make products one loves. 
Finally, it is important to have clear learning objectives. These objectives will directly impact the roles and companies to which one applies. If one wants to create scalable machine learning systems, a role in data science is not probably the right choice. Instead, one should target ML engineering roles. One should also identify & interview with companies that a established positive reputation of using machine learning systems to solve complex problems. If one wishes to work directly on business problems, one should target analyst or "quant" roles at an entirely different set of companies. There are similarly other points to consider here.
To summarize things, in order to successfully make a career in data science, one needs to set some goals, be aggressive, know one's worth, figure out what one likes and repeat this process till there is complete clarity.

Source: HOB