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How to Prepare for a Data Science Interview

Woman conducting tech interview with female candidate

ARTICLE SUMMARY

Darya Petrashka, Data Scientist, shares her advice on how to prepare for a data science interview.

Passing a technical interview could be challenging. It is a standing aside art of self-presentation that is often unrelated to day-to-day work. In the highly competitive field of data science, preparing for a technical interview is crucial to landing your dream job.

This article will provide valuable tips and strategies to help you prepare for your next data science interview, from brushing up on technical skills to honing your communication and problem-solving abilities.

PREPARE TO TALK ABOUT YOUR EXPERIENCE

Before the interview, sit down and think about your career journey. First, create a small overall story. It will be extremely helpful if you did a career transition. When a recruiter sees that you have a Master’s degree in Linguistics, but your previous job position was that of a Data Scientist, it may raise questions (whereas transitioning itself is not bad, it can be even beneficial). To address any concerns, it’s important to provide context and explain the reasoning behind your decision. People love stories, so share your journey and why you made a conscious choice to change careers.

Talking about previous experience, try to use the XYZ formula: “Accomplished [X] as measured by [Y], by doing [Z].” For example, instead of saying: “Worked with other Data Scientists on time series project”, say: “Collaborated with 4 Data Scientists on forecasting market trends of more than 50 products, resulting in saving $1M.”

TECHNICAL PART

The important part of an interview is to show your technical skills. Go through the ‘Skills’ section of your CV and be sure that you can answer basic questions about each skill you listed there. There are a lot of ready-to-use question lists like this one, so you can check yourself and see if there are any white areas. Review terms that tend to be mixed or forgotten quickly (like Precision/Recall, I often confuse them).

Practice explaining complex subjects in simple words, it will show that you understand the topic + are capable to express it to non-technical peers. You can find a big inspiration on how to do it in the StatQuest channel. 

It is crucial for Data Scientists to not only know the technical part but to understand how it can be applied to solve a business problem. So for example, when answering a question about different types of clustering, give an example of how you used them or how they can be used in grouping a company’s clients.

SHOW THAT YOU ARE CAPABLE TO LEARN FAST

Data Science is a high-speed developing field and almost always data professionals differ in their skills and toolsets. There is nobody who knows anything. If a job position requires let’s say, experience working with Azure, but you tried AWS or GCP only, you can mention it and explain how the skills and knowledge you acquired can translate to the required platform. Additionally, you can highlight any examples of when you quickly learned a new technology in the past, and how you were able to apply it effectively to solve a problem or complete a project. This shows that you are able to apply new technologies, which is a valuable trait in the ever-changing field of Data Science. 

DON’T FORGET TO STUDY THE COMPANY

Before going into an interview, it is essential to research the company and its website thoroughly. This will give you an understanding of the company’s services and products. By doing so, you can tailor your answers and questions to align with the company’s values and goals, showing your interest and understanding of the organization.

Don’t be shy to ask questions. It is often hard to understand from the position requirements what your day-to-day job will look like. So the good question to ask is: “Let’s suppose you hire me as a Data Scientist for your department X. In one year, how is the excellence in this role will look like?” This, on the one hand, will let the interviewer imagine you in the role, and on the other hand, will give you valuable information about what exactly is expected from you.

Remember that interview is a two-sided process. Not only the company decides if you suit well, but you decide if you want to be a part of it. Search for potential red flags: notice how communication is built between different employees, whether you are getting pressed during the interview, etc. If you feel uncomfortable, think twice: do you want this environment to be your daily reality? 

MAKE A PROFIT FROM EVERY SINGLE INTERVIEW

Sometimes you can realize that you are not really a good fit for a role. Try to turn this situation into a beneficial one: you got a chance to practice your interviewing skills, learned about a new company, and maybe spotted some gaps in your skills. If you have a friend who fits better, you can always ask if the company is interested in your recommendation. The hiring process is not cheap, and most companies offer generous bonuses for recommendations.

PRACTICE, PRACTICE, AND PRACTICE!

If you feel worried or scared, it’s okay – most people are nervous during the interview. One good trick is to say that you are very excited to be there, that will help you shift your focus from your anxiety to a more positive mindset. Take a bottle or glass of water and use it as a pause when you need time to think about a question. One more way to tackle anxiety is to have more practice. You can use mock interview resources as pramp

Remember that the interviewer is likely aware that many candidates experience nerves during interviews, and they are not there to judge you for it. They are simply looking for the best fit for their company. 

In conclusion, preparing for a Data Science interview requires a combination of technical skills and communication abilities. With practice and the right mindset, you can ace an interview and land your dream job. So take a deep breath, stay positive, and do your best to showcase your skills and experience. Good luck!

About Darya:

Darya Petrashka, data scientist

AWS Community Builder, works as a Data Scientist at SLB. She is passionate about data and its usage for problem-solving. The area of interest includes classical ML and NLP, as well as working with AWS services. An eternal student, she likes taking part in online schools, courses, and workshops. She shares insights on her Linkedin page and medium blog.

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