How to prepare for a data science interview

You’ve spent months studying data science, now it’s time to find a job in the industry. Fortunately, companies all over the world are looking to hire data scientists -- and fast. According to LinkedIn’s 2017 U.S. Emerging Jobs Report, machine learning engineer and data scientist positions grew 9.8x and 6.5x respectively in the past five years. Candidates who are trained will have a bevy of vacancies to choose from when ready to seek out a new position in the field.

Though there are many open data science positions, the interview process can be rigorous. The technical tests are tough, but hiring managers still look for soft skills when hiring candidates as well. The LinkedIn report found that soft skills still matter even for highly technical positions. HR still screens for adaptability, leadership, and the ability to learn from others when hiring for data scientists.

For anyone ready for a data science career or ready to switch jobs, here is how to prepare for this interview process. Though not every company is the same, this overview provides the general guidelines for what to expect when seeking a data science position.

Stages of Data Science Hiring

The first stage is a phone call, as with any job. The HR manager will call the candidate to screen out anyone that is clearly not a fit. Often, this first phone call is not necessarily about technical abilities but rather to see if the candidate can carry on a conversation, that he or she understands the businesses, and that the potential employee did some research about the company. The HR person taking this call wants to hear your interest in the company as well, so ask insightful questions but don’t worry about getting too technical, as you’re not on the phone with the chief data officer.

The second stage is likely a call with a team member. At a large firm, the data scientists might be split up by specialty so the call will be with a mid-level team member with your skill set. At a small firm, however, data scientists are sparse so you may end up on a call with the CDO sooner rather than later.

This call will evaluate both technical and soft skills, so be prepared to take on over-the-phone coding challenges. The interviewer(s) will ask about past experience and will want you to walk them through projects. Furthermore, your mathematical, statistical, coding and analysis skills will be put to the test with pseudo-problems proposed and well-thought-out answers expected. Interviewers will want to hear your process, how you work through problems, what additional questions you ask, and whether or not you can get the correct answer. Furthermore, the interviewer will want to listen to how you deal with a semi-stressful situation.

An option stage that some companies take is an online test; they may send the test after the first or second stage. At times, businesses will want to weed out candidates, seeking those who can complete the test while letting go of the ones who do not have the technical ability to do so. Usually, the tests are timed so companies can discover the most compatible candidates.

The next stage is usually on-site. Especially if the candidate is not local, the interview might be an all-day experience. The on-site interview will likely include a meeting with the head of HR or head of hiring, the CDO, and a team and/or team members. An additional technical challenge or test could also be administered this day or after the interviews are done.

On the technical side, the interviewers will throw different scenarios at you and watch you complete the problems. They might also ask you to explain different concepts to a business leader to see how you can take data and translate it for practical use. Those soft skills are fundamental so that the interviewer can see how you’ll fit on the team.

How to Prepare for the Interviews

Preparation is crucial for data science interviews. If you cannot find interview questions already listed on sites like Glassdoor (for example, here are Amazon’s questions), review potential technical questions for each level on other data science interview sites to help understand what might be asked. If you worked with a recruiter, ask them about the structure of the interview or any preparation/tips they can give provide.

To really prepare, try spending time on HackDSInterviews. The site allows users to practice skills in SQL, Python, R, Hadoop, and Spark. Candidates can review the over 1,000 coding challenges, theory problems, and case studies to be fully prepared for any type of data science interview.

Practice any skills you know you’ll be tested on so that when you perform tasks in front of interviewers, your processes are smooth. While there will be a time when you have to sit and think through a problem, make sure it’s not one you should know how to do. You will have an idea about which skills the company will test based on the position.

Participate in mock interviews to run-through how you’ll answer basic questions or even how you’ll pause to think about an answer. Some of the technical questions might cause you to stop and think, but you want to sound confident and not shaky. If possible, talk through scenarios with other data scientists to rehearse questions for the interview.

Formulate questions to ask each person during each interview. It’s important the interviewers know you are an enthusiastic and interested candidate that is eager to learn more. Lastly, follow up with a thank you note to show your dedication to landing the position. Following these tips and knowing what to expect can help you get a new or first job in data science.

This blog was originally posted on SwitchUp. 

Founded in 2013, the NYC Data Science Academy offers the highest quality in data science and data engineering training. Their top-rated and comprehensive curriculum has been developed by industry pioneers using experience from consulting, corporate and individual training and is approved and licensed by the NYS Department of Education. The program delivers a combination of lectures and real-world data challenges to its students and is designed specifically around the skills employers are seeking, including R, Python, Hadoop, Spark and much more. By the end of the program, students complete at least four real-world data science projects to showcase their knowledge to prospective employers. Students also participate in presentations and job interview training to ensure they are prepared for top data science positions in prestigious organizations. For more information visit http://nycdatascience.com.