The interview always begins, these days, through a square box on a computer screen. To face a panel of judges and engage in hours of technical questions and cultural fit scenarios can be a daunting task. According to a recent report by Vox, job interviews have become longer and more demanding, with increasingly convoluted requirements.
But through adequate preparation and enough practice, candidates can successfully navigate the job-seeking journey with confidence. Just ask Seth Johnston, data scientist at Simply Business, who has run the gauntlet multiple times. Now, having had enough experience to land a job at the Boston-based insurtech company, he’s used to the challenges and formats that data science interviews present.
Simply Business provides small business insurance as a streamlined digital service, which naturally involves a crucial data operation. Landing the impactful job may seem intimidating, but Johnston has maintained a few key strategies adaptable across data science interviews.
“I have found that it’s critical to do considerable research before an interview since it enables you to ask more pertinent questions,” he said. “If I’m unsure of the answer to a question, I openly explain that and ask the interviewer about it.” For Johnston, it comes down to always being authentic.
For a more detailed breakdown of what to expect for a data science interview, Built In sat down with Johnston for his guiding principles for interviewing in a competitive field.
Tell us a little bit about your first experience interviewing for a data science role.
My interviews for data science roles have followed similar formats. My first interview was with a small power company. For the first part, I met with the human resources about my work experience, professional background and the open position. I then met with my prospective manager and colleagues. In this interview, I was asked about my experience with SQL, past projects and was provided with a project description where I was asked how I would approach solving.
Some companies also include pre-screening questions that involve video recording answers to technical prompts, while others provide a dataset and problem statement. The latter is where they ask you to take a few days to analyze the data, build a model and present the findings. My biggest takeaway from interviewing has been to be authentic. The data science field is quickly evolving and it’s important to show you are willing to learn.
My biggest takeaway from interviewing has been to be authentic.”
What is the most important thing you do to prepare for a data science interview, and why?
My interview preparation involves extensively researching the company and its data strategy. I like to take the time to refresh my fundamental skills like statistics and performance metrics because it helps me prepare for any technical questions that may come up.
I have also found it helpful to read through the job description. If there is a system or method I’m not familiar with, I start researching and learning it. This allows me to discuss the concept and show that I can learn quickly, while being honest about my limited exposure. Finally, I prepare questions to ask during the interview. This is the part of the preparation that I find most difficult, so while I always prepare several questions to ask, I also make sure to listen closely and make an effort to ask follow up questions based on our conversation.
What advice do you have for someone preparing for a data science interview at Simply Business?
Simply Business values learning and authenticity. I would tell someone who is interviewing at SB to be honest about what they know and don’t know, and to showcase their willingness to learn.
