Insider's Guide to Acing Data Science InterviewsBy:
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 2020 U.S. Emerging Jobs Report, skills related to Machine Learning, Deep Learning, TensorFlow, Python, Natural Language Processing, etc. seen more than 70% annual growth. According to an IBM survey, the openings for data and analytics talent in the US will continue to increase, reaching 133% growth in 2020, and creating more than 700,000 openings. Qualified candidates will have a multitude of vacancies to choose from when ready to seek out a new position in the field.
Though there are many open data science positions, job seekers should be prepared for a rigorous interview process. The technical questions will be tough, but hiring managers will also consider soft skills when hiring candidates. A recent LinkedIn report confirmed that soft skills still matter even for highly technical positions. HR departments consider candidates based on their capacity for adaptability, leadership, and learning from others.
For those professionals who are ready to break into data science, we’ve compiled a guide to help you get started! In this guide, we’re sharing a preview of the stages of data science hiring, and interview preparation tips!
Stages of Data Science Hiring
1. Phone Screening with HR
As with any job, the first step in an interview process is a phone call. In most cases, an HR manager will call candidates to screen out applicants who are clearly not a strong fit. This first phone call is not frequently used to determine technical abilities, but rather to see if the candidate is personable, understands the business, and took time to research the company. The HR representative carrying out these phone screenings may also want to hear why applicants are interested in being employed with the company, so it’s important to ask insightful questions. But, don’t worry about getting too technical, it’s unlikely that an initial phone screening will put you in touch with chief data officer (CDO)!
2. Phone Screening with team member
If a candidate successfully navigates an initial phone screening, the nextstage is likely a call with a team member. At a large firm, the data scientists are likely split up by specialty, so it would be reasonable to expect this call tobe with a mid-level team member with a similar skillset to yourself. At a small firm, however, data scientists are sparse, so you may find yourself speaking with a chief data officer type employee sooner rather than later.
Regardless of who you speak with, this call will evaluate both technical and soft skills, so be prepared to take on over-the-phone coding challenges. The interviewer(s) will likely ask about past experience and will want you to walk them through notable projects you have participated in. Furthermore, your mathematical, statistical, coding, and analysis skills will be put to the test with pseudo-problems. In these cases, critical and well-thought-out answers are expected. Interviewers will want to hear your process and how you work through problems. They will also be interested in what additional questions you ask, as well as whether or not you can come to the correct solution. Furthermore, the interviewer will be observing, and evaluating how you deal with a semi-stressful situation, so it is important to maintain composure and express yourself clearly, and thoroughly.
3. Online Test
After phone interviews, some companies also opt for an online test. Employers use these tests to determine which candidates can prove their technical abilities, and alternatively, weed out unqualified applicants who may have performed well in phone screenings, but have yet to demonstrate their knowledge. Usually, the tests are timed so it is important to prepare.
4. Onsite Interview
If the company determines that they have found a good candidate, after phone screenings and potential tests, the next stage is usually an on-site interview. These type of interviews are often all-day affairs, especially if the candidate is not local. 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 could also be administered this day or after the interviews are done.
Aside from meeting with numerous team members, technical interviews will be used to challenge applicants with different scenarios. During these challenges, interviewers will observe the candidate’s process, and ask questions about their rationale for approaching problems the way they do. They might also ask how the candidate would go about explaining the relevant concepts to a business leader. This helps to demonstrate the candidate’s ability to translate data into practical use, a fundamental soft skill that helps illuminate how the candidate will perform, and fit into the team.
What Is the Goal of an Interview?
Before we begin dissecting interview questions and preparation techniques, we first need to have a solid understanding of the purpose of an interview. To put it simply, an interview is designed to test whether or not a candidate is a good fit for a position. However, there are many different aspects to consider when determining candidate fitness.
When applying for a data science position, considerations could include:
1. Technical Skill
- How proficient are you at the techniques used in data science?
- How solid are your coding skills?
- Are you able to demonstrate your problem solving skills?
2. Social Fluency
- How comfortable are you interacting with others? This includes daily interactions but also includes things like conflict resolution and expressing disagreement in a professional manner.
3. Cultural Fit
- Do your values align with the values of the company?
- Would you be happy working in the environment the company has created, and would the company be happy having you?
Most, if not all, interview questions can be categorized into one of the above three topics. A good first step in preparing for an interview is to make sure you know which questions belong to which category. For instance, is a question aimed at determining your technical skill, or your behavioral tendencies? Knowing the goal of a question can help lead you to a suitable answer.
How to Prepare for the Interviews
While the interview process can be very intimidating, following the below tips and knowing what to expect can help you land a job, or further your career in data science. Interviews are a necessary step on the road to starting a new job, and in order to prepare for such a technical position, preparation is key. There is no alternative to learning and practicing the material, but there are certain ways you can prepare yourself, that will allow the interviewer to see you as an ideal fit for the position.
This below approach to interview prep can help sure you land that perfect job!
1. Test yourself with sample questions
It’s no secret that preparation is crucial for data science interviews. One useful tool for interview preparation is to locate sample questions. If you cannot find interview questions specific to your potential employer already listed on sites like Glassdoor (Like Amazon’s questions), try reviewing potential technical questions 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.
2. Check out HackDSINterviews
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.
3. Practice technical skills
It is important to practice the skills you know you’ll be tested on so that when you perform tasks in front of interviewers, your processes are smooth. While you will inevitably be challenged with questions that cause you to pause, preparing for technical questions in advance can minimize this occurrence. The quicker you recall and demonstrate solutions, the better.
4. Prepare with mock interviews
Participating in mock interviews allows you to run-through how you’ll answer basic questions, and even condition you to maintain composure as you pause to think about an answer. Some of the technical questions might cause you to stop and think - which is expected - but it is important to sound confident and composed as you deliver a hard-fought solution - not shaky. To prepare for instances like this, talking through difficult scenarios with other data scientists allows you to rehearse responses, and practice speaking with confidence.
5. Compose thoughtful questions
While demonstrating your own skills is the number one goal of your interview performance, it is also a good idea to formulate questions to ask each interviewer during your one-on-one time. You want the interviewers to know that you are an enthusiastic and interested candidate that is eager to learn more.
6. Bonus! Follow up with a thank you note
After you’ve completed your interview process, and used the tips above to showcase your hard and soft skills, the icing on the cake is to follow up with a thank you note. This simple act helps to show your dedication to landing the position.
There you have it! Now that you understand the stages of interviewing, and how to prepare, you’re ready to get started. If you’ve yet to skill up, read on to learn how NYC Data Science Academy can help you become a strong applicant for data science positions!
Becoming a Qualified Candidate
At NYC Data Science Academy, we provide technical and strategic training on full- and part-time schedules. We offer educational, training and career development services dedicated to delivering a wealth of experience in data science - giving us a great insight into what a successful portfolio looks like. Read more about us, and check out our reviews on SwitchUp!
By the end of the program, NYCDSA students complete at least four real-world data science projects like the ones above. These projects showcase students’ knowledge to prospective employers.
In addition to projects, students also participate in presentations and job interview training to ensure they are prepared for top data science positions in prestigious organizations.
93% of NYC Data Science Academy students are hired within six months of graduation, and alumni are currently working at companies like the ones seen below.
It’s impressive, we know!