Do you have an upcoming job interview for a role in data science? Here’s what to prepare so that you can prove you’re the right candidate. Search for more data science roles here.

What to consider before your job interview

Make sure you know what will be expected of you in the interview. You can do this by checking with your recruiter. If it’s the first round of interviews or at an early stage in the recruitment process, you might not need to delve too deeply into your technical knowledge or discuss your experience in fine details. In this instance, it’s important that you’re ready to tell the story of your career clearly so that the interviewer is aware of what you can bring to a company.

Alternatively, if you’re meeting your potential manager or head of department, you’ll usually have to prove your hard skills. As discussed in a previous blog, the interviewer will want to know about your sector experience, technical skills and the ability to solve tough problems using data; this can vary from company to company, but can include technical questioning and sometimes coding tests, so you need to be prepared.

Regardless of how far along you are in the hiring stages, you’ve got to know about the organisation. Research the key trends in the organisation’s industry sector, as well as what competitors are doing, so that you have a greater idea of what would be expected of you in the role and, therefore, what you need to prove you can do.

Proving your skills as a data scientist

In order to convince your interviewer that you’re the right candidate, it’s best to make sure you’re ready for the following:

• Programming: Make sure you highlight your technical skills, including examples of where you have used them. You should have listed the programming languages with which you’re comfortable on your CV, so you may be asked to demonstrate your knowledge of these.
• Data handling and SQL: It’s highly probable that the role will require you to handle vast amounts of data. The interviewer could ask you to give examples of occasions where you’ve done this in the past, while you might even be tested with a technical exercise.
• Maths and statistics: If you’re entering the field of data science and have less experience, be prepared to talk about your mathematical background and statistical concepts.
• Machine Learning Algorithms: You will need to be comfortable talking about basic machine learning algorithms that fit specific problems.
• Projects: Have examples prepared of projects that you have delivered and the problems that you have solved. What did you do? What was the result? Was it successful and, if so, how did you measure that? It’s a really good idea to have a portfolio of these projects that you can showcase in an interview. Just make sure you are not breaching any previous confidentiality rules at your past/current organisations.

If you interview is a technical one, you can find more advice here.

Questions to ask your interviewer about a role in data science

You should always have questions to ask your interviewer as, most of all, it shows that you’re truly interested in the role and company. If you’re having any doubts, it’s also the time to confirm/remove these.

As discussed here, in the past organisations have sometimes hired a data scientist without a coordinated strategy. As such, good questions to ask your interviewer include:

• “How new is this role?”
• “How will my work be informing the organisation?”
• “Can you confirm that there are previous instances of the organisation using the results that the data scientists have delivered? To what extent has it proved useful?”

Our Global Head of Hays Technology, James Milligan, has provided some other questions that you should be asking in your job interview here.

Looking for a role in data science? Find your next job here.

Author

Martin Pardey
Senior Business Director Hays Technology – South East UK

Martin Pardey is a Director for Hays Technology in the South East, the World’s largest specialist recruitment company. Martin has 15 years’ experience at Hays, specialising in the last few years in the Business Intelligence and Data Analytics sector.