How to Identify a Good Data Scientist in a Job Interview?

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Data Scientists continue to be in high demand, so when a company is ready to make the leap to modernity and start using Big Data , hiring a good professional will be crucial to the success of Data Science and Big Data Analytics projects, and it is important to know How to Identify a Good Data Scientist in a Job Interview . Every day we receive more recruiters here at Data Science Academy and these professionals have many questions about the ideal profile of a Data Scientist , since this is a new and growing profession. This article aims to help recruitment and selection professionals, providing valuable tips and insights. Check it out.

A Big Data Analytics project usually involves a considerable budget and a lot of visibility within a company, and every Data Scientist dominican republic email list wants the company to believe that he or she is the kind of genius who can generate insights that change an entire market “just” by creating a few predictive models. And some really can. But others can’t.

If your company is ready to hire a Data Scientist , there are some important questions to ask to ensure that you hire the right professional. These questions can be crucial during the selection process for a good Data Scientist . Let's take a look at them.

Does the candidate have strong programming skills?
A Data Scientist needs skills to not only analyze and visualize data, but also manipulate it. A statistician who analyzes and interprets a data set is very different from a Data Scientist who can write a data analysis procedure using languages ​​such as R, Python, Scala or Java, to collect, process and explore data, in addition to building Machine Learning models that can be processed in distributed environments across a cluster of computers and with large data sets. And Data Scientists are also responsible for developing Artificial Intelligence systems, which requires a programming language. Knowing a language well is essential for those who intend to work with Big Data Analytics and AI. Something that companies have been doing frequently is asking the candidate to present a portfolio of projects or in some cases, to create (during the technical interview) a data analysis procedure, proving their skills. If you are a recruiter, consider including this type of verification and ensure that the candidate masters at least one programming language.

Does the candidate excel at producing analysis for computers or humans? (And which do you need?)
There are two main types of Big Data analytics : those whose end user is just a computer and those whose end user is a human. If the end result is a machine learning algorithm to, for example, choose which ads to display on a website or perform automatic stock trading, your analytics will be for computers. If, on the other hand, a human being makes a choice based on the analytics, your analyst needs a different set of skills, mainly being able to tell a story through data and provide good visualization of that data. It is difficult (but not impossible) to find the same professional with both skills and it all depends on the purpose for which the professional is being hired.

Can the candidate provide concrete examples of how they have improved a business process through their work?
As with any position, you expect to see real-world examples of how the candidate has successfully implemented improvements to a business process. A data scientist should be able to present their portfolio of projects or at least describe in detail the projects they have worked on! Every analysis process exists to solve problems and this skill should be tested during the selection process. Candidates who are not focused on problem-solving do not understand the importance of data science and may not bring value to the company. During the selection process, test whether the candidate has worked with different business areas, whether they know how to conduct a requirements gathering meeting, whether they always present their portfolio as a problem solver and which processes they have been able to improve or even rebuild.