![]() ![]() This is because certain jobs emphasize some data analytics skills over others. Too many data science professionals lean heavily on the knowledge of the field at the expense of showing their ability to be personal and work with others.ĭepending on the job listing, data science interview technical questions typically cover the following subjects: data analysis, machine learning, statistics and artificial intelligence. Prepare to cite engaging examples and emphasize your educational background. Take time to prepare these answers before you meet with the hiring manager, carefully consider how you became interested and started in data science, and provide your perspective on how the field has impacted you and helped others. This question allows you to have a second opportunity to point to an accomplishment in your resume that solidified your commitment to the field, and also an opportunity to tell a human story about how you came to be a data scientist. It’s sometimes asked of candidates whose resumes show an unconventional career change or gaps. Tell us why you chose a career in data science. This question is asked to test how committed you will be to the role and how long you plan to spend in the field. For a data science job, this question is a great opportunity to discuss some of your most notable accomplishments in previous jobs, allowing you to introduce your data analysis expertise found in the job description. The interviewer on the company’s data science team has already reviewed your resume but is looking to test how well you can sell an idea, whether you’ve read and understood the job description, and if you’re prioritizing your most relevant expertise to their needs. Every data science master’s student has experienced this question during their career. The aim is to allow the interviewer to know you better, understand your personality and assess your strengths and weaknesses. Therefore, before reviewing your technical expertise, these questions are commonly used to open up an interview. Structural/Cultural Fit Interview QuestionsĬompanies need to know that the data science professionals they are investing in will complement the skill set of the existing team. ![]() After reviewing these questions, you’ll better understand how to approach the data science interview questions related to the ones listed below. The goal from the company’s standpoint is to test the applicant’s knowledge of data science and their fit for the company. ![]() Each category will cover some commonly asked data science questions within that section.ĭuring a data science job interview, a mix of technical and structural questions will likely be asked. This article will introduce some of the most common data science interview questions asked during an interview, as well as some helpful insights to help make sure you arrive at your interviews more prepared than the other applicants.įor easier classification, the questions will be broken down into several categories. In addition, preparation involves reviewing common data science interview questions that companies typically ask, having an idea of how the teams are structured, and the backgrounds of the stakeholders who will interview you and make the hiring decisions. Therefore, preparation is paramount. To ensure that you’re prepared for a data science interview, it’s essential to spend time understanding what past interviews for data positions entail. For your interview, you’ll need to prepare for the possibility of completing a coding challenge or technical interview. The interview process can be long, and data scientists need to understand the business challenges of the company you’re interviewing for in addition to the technical. There are thousands of search results for “data science interview questions” and lots of candidates helping each other out on message boards, social networks and job review sites. Data science interviews are some of the most challenging in any industry. ![]()
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