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5 Most Common HR Interview Questions & Answers for Data Scientists

You cleared the technical round for a company and now it’s time for the HR interview… Your mind is probably buzzing with questions regarding your interview.

Every company conducts an HR round of interviews to judge your capability, attitude, personality, strengths, weaknesses, intent, etc. It’s usually conducted after the technical round and has all kinds of questions.

So let me make it easier for you…

Here’s a list of questions that are most commonly asked in the HR interview:

Each question will be answered in detail below

Why do you want to work here as a Data Scientist?

This is purely to understand your motivation and reason to work in this particular company. HR wants to understand your choice and motive for applying to this position.

HR also wants to know your intent behind joining the specific company i.e. they don’t want people who will work for 6 months and then leave. They’re looking for long term employees because the whole hiring process is very tedious and consumes a lot of the company’s resources.

The answer you give should reflect your love for Data Science and the inspiration this organization gives you to pursue your love.

You need to be able to communicate your passion for the subject. You can say something like, “I have a deep interest in data mining and analysis, and I also admire the company’s technological capabilities. I look forward to combining both and delivering excellent performance.”

A good tip that I can give you is that, do as much research as you can about the company (there’s no such thing as too much research). This will help you include specific things about the company in your answer, in turn, creating a great first impression with the HR.

Has your prior experience contributed to preparing you for a Data Scientist role?

Now, depending on if you have relevant experience or not, you need to answer this question skillfully. Your soft skills are being tested here.

Because, being a data scientist does not only mean having sound technical skills but rather having enough skills to be able to communicate their findings in a way that the main decision-makers can understand, work in a team or be able to lead a team.

And if you have work experience then you can specify how that experience has helped you grow as a Data Scientist.

How do you overcome challenges?

Now, these challenges could be task-based or even work culture related. This is to gauge how you approach problem-solving, and how you approach the resolution to a conflict.

You may often encounter stressful situations, which is where interpersonal skills come into play. When working in a team or a group, such situations can affect the work dynamics. You need to stress the importance of teamwork.

You can answer something like, ‘I would acknowledge their contribution and findings, come up with a conclusion and invite open feedback’. This demonstrates leadership skills and maturity.

How would you work with large data sets?

This question is asked to make sure that your basics are absolutely clear. As a data scientist, you may have to come across huge volumes of data to work with.

Especially in a huge company, you’re constantly working with huge chunks of data. This question tests your knowledge of what kind of methods you’ll use to organise and clean large datasets. These methods are important as the organised data is then used to solve business concerns and used for deriving insights.

You may name some tools and methods which are used to clean data, which will further highlight your knowledge.

Where do you see yourself in 5 years?

This is a tricky question, to check how much you have mapped out your career path. You most definitely should not say things related to quitting the company (that should be very obvious).

Instead of answering with a specific role, try and give benchmarks with respect to work experience. In the sense, you can give work milestones like gaining experience of data visualization tools, a better understanding of SAS, Hadoop, mastering Python, working on the tableau platform, etc. Giving solid goals like this will give off an excellent impression, and the goals would also sound achievable.



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