At Interview Query, we love to hear from those who’ve successfully landed jobs in the data science field. To help the rest of our community, we’re sharing their career path stories and approaches to interview preparation.
This week we caught up with Owen McCarthy, who joined Intuit after completing his Bachelor’s in Data Science at UCSD in 2020 and followed an unconventional path. We discussed his personal journey, tips for getting to the interview stage of applications, and the Intuit interview guide.
I attended University of California, San Diego, which didn’t have a data science major when I started. Instead, I blended the computer science and business majors to build a bridge between them and was lucky that they kicked off a dedicated DS program my sophomore year. I was part of the inaugural cohort to graduate from this new program and also snagged a business minor on the way out.
I started off looking for roles in data science, with a strong focus on natural language processing. OpenAI and GPT-2 were just coming out, and by GPT-3, I knew that this was a field worth putting a real bet on career-wise.
The roles that companies were hiring for wanted more extensive educational backgrounds than mine, but I got lucky with a data science program with a company called Barisk.
The program was designed as a rotational, where every 18 months, you would be moved to a new business sector and geographic area. After three rotations, or three and a half years, you come out as a senior data scientist, a project manager, or a data science manager.
Even though it was still geared towards master’s graduates, I had noticed a small input at the bottom of the application which allowed you to communicate extra information to the hiring managers. In this way, I was able to overcome the lack of higher degree by speaking with the team more directly about my interest in the field.
I did not go through the regular channels to get the interview. I paid for LinkedIn Premium, searched for data science recruiters, and emailed them directly if they had an email in their bio. You can also try to hunt their email down on the web if you know their name and company. There are quite a few websites for that.
I would email these individuals, letting them know that I was interested in their group and that I had experience as a data scientist. I made sure to attach my resume as well, and saw quite a bit of success with this method.
Intuit eventually got back to me on a senior data science position, and I started preparing for my interview there!
Preparing for data science interviews can be tricky since there’s so much breadth of content. You’re being tested on Python or R knowledge, SQL, small data structures, stats and probability, machine learning, and some business or product questions. There is just so much out there to know.
For the current role, I studied SQL for my interviews, since that’s what they advertised they were looking for in the job listing. I also reviewed quite a bit of the theory behind general machine learning algorithms. Some examples are:
Knowing these foundational machine learning concepts proved to be really helpful.
Lastly, my big tip would be, while it’s essential to practice Python and SQL, don’t overlook the importance of preparing for behavioral interviews. What I observed with my data science colleagues is that they typically ace the technical portions, but will get leveled if their behavioral answers aren’t strong. Always use the STAR method (situation, task, action, and result), be thoughtful, and answer the prompt.
Recruiter Call (30 Minutes)
This was your regular thirty-minute call, to determine what roles I was qualified for and if I could work from the Mountain View campus or remotely. We also discussed salary and benefits for certain roles, which I stayed non-committal on, pending the final position and scope.
Technical Screening (1 Hour)
I had one Python question and two SQL questions, which took around 30 to 40 minutes to complete.
There was then around 20 minutes to discuss my background.
If there is extra time, they’ll likely ask some filler questions on Python or general machine learning, basic topics like bias, variance, trade-off, boosting, or bagging.
The Four Round Interviews (3-4 Days)
Round 1: Solution Creation and Demonstration
You are given a problem, and you need to create a machine learning solution to demonstrate and present. That presentation is to the team and needs to be around an hour in length. That’s a lot of time to be speaking to this solution, so you definitely need to do quite a bit of prep for it. They also spent around ten minutes on the candidate’s background as a chance to get to know you better.
Round 2: Skip-Level Manager (The Boss’ Boss)
This interview is with your supervisor’s manager and is all about stakeholder management. They want to know how you go about product-related projects, how you work with others, your formal or informal leadership style, and what type of manager you’re looking for. There might also be a few business questions peppered in here.
Round 3: Technical Interview
Another hour-long technical assessment, again focused on conversational-style questions, Python, SQL, and your background again.
Round 4: Hiring Manager
This is just a typical behavioral interview, with nothing too out of the ordinary. Don’t forget your preparation and the simple STAR framework.
Note: The exact order of the final four interviews may be different for each candidate.
The hardest part for me personally was getting to the interview. Applying for me personally was such a chore. The hardest thing is just starting anything. But that first step can be small, investigating a project, sending that email, talking to a connection in the space, the point is to get the ball moving in any way possible.
Be on guard and avoid falling into the trap of practicing the things that you already know. If you’re comfortable with a certain type of SQL or Python question, you’re more inclined to practice it, but you might be neglecting the things that you aren’t so good at or don’t know. So really just kind of getting a good understanding of the actual preparation that you might need because it’s tailored differently to each individual, for sure.
Your competition in data science interviews will be strong technically but are often weak when it comes to the behavioral portion. Set yourself apart from the pack by practicing your storytelling, and answering the prompt directly, and don’t be afraid to lean on frameworks like the STAR method (situation, task, action, and result).
If you are a current or recent master’s student, or work at a university and are trying to find tools to help your students succeed, check out our University Resource Page!
For more guides and questions for Intuit, check out our interview guides and technical questions, for the different roles they have hired for in the past!