ZachOverflow

Why Don’t I Feel Prepared In Data Science Interviews?

Most data science candidates are unprepared for the most important — and basic — job interview question of all.

ZachOverflow is a recurring column in which I attempt to answer one frequently asked data science question thoroughly and honestly. No oversaturated topics. No listicles. No clickbait. Just my (mostly) unfiltered responses based on professional experience, technical exposure and, yes, the occasional unsubstantiated opinion.

Over six years ago, I sat at an oak table on the 9th floor of the New Yorker Hotel in midtown Manhattan, flashcards scattered around me, my phone in front of me, open to the voice memo.

As I stopped the recording, I grimaced. The timestamp? 2:30.

“That one was a little long, wasn’t it?”

My friend, T, looked up from his avalanche of emails and nodded.

“You just have to remember”, he said. “You’re not just answering an interview question. You’re telling your story.” At this point I’ve told my story nearly 20 times, participating in the most grueling interview prep I’ve experienced before or since.

That’s what happens when you ask a Wall Street intern for interview prep tips. While the stereotype of Wall Street is the “killer” instinct, in reality it’s an equally ruthless approach to preparedness and anticipation that candidates across industries should strive to adapt. Those successful in that industry treat interviewing like a job and the prep like a final exam.

When it comes to interviewing for data science positions it’s so easy to become overwhelmed by technical requirements, the prestige of your prospective employer and the velocity of questions. So many career advisors tell you to prepare for your interviews. But even then I’d bet that you and many other candidates still feel unprepared in the moment. I know I did for the longest time.

And it’s not because you didn’t “study.” Plenty of folks can pass a technical screener or a few HackerRank questions and bomb the interpersonal interview for one simple reason: They focus too much on answering the question and not enough on framing themselves as the ideal answer.

The one question that T drilled me on wasn’t anything special.

It was the first question any interviewer, including data science leads, will ask: Tell me about yourself.

The secret to answering this question isn’t just to vomit your list of qualifications or resume bullet points.

The secret is to filter your introduction to include only experiences relevant to the job, demonstrate knowledge of the company and field, desire to work for that particular organization and, most importantly, act as an organic spring board to further questions.

The best interviewers, especially these Wall Street folks, don’t just answer the first question. They take control of the interview, immediately displaying the confidence, intelligence and charisma needed to succeed in a competitive industry like finance or data science.

And, believe me, none of this comes naturally.

It’s all about preparation.

If your prep doesn’t include at least an hour or two of research on a company’s history, achievements, mission and power players, you’re not adequately prepared to talk about how you’d be a fit.

Aside from doing your research on your target company, you need to nail that interview “first impression”, i.e. the tell me about yourself (or any variation) question.

To do this, you need to frame your response as a story. Employers, being human, are naturally inclined to connect with stories of all types.

If it helps, you can break “tell me about yourself” down into these questions:

• What is your (relevant) employment history?
• Why do you want to work for the company?
• What skills or distinctions do you have that would make you an asset?

A mentor of mine in television production once broke it down in a simpler way:

• Why (company)?
• Why you?

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My data science student friend confessed to me that he gets psyched out in interviews by the technical questions and the perceived intelligence of his interviewers, both of which can be intimidating. However, like a good piece of writing (or a cover letter), beginning with a strong introduction will set you up to confidently address more difficult questions later in the interview.

Taking the above questions, I’ve come up with an introduction template that positions you to “tell your story” rather than spout resume bullets. Since both industry professionals and students read this blog, I’ve broken down responses by two experience levels.

New Graduate

My name is (your name). I’m a current (your year in school) at (your school) where I specialize in computer vision applications, a passion I discovered while interning at (company) last summer. Inspired by (target company’s) mission to use facial recognition to enhance home security, I’ve been working on a side project to create a facial recognition product prototype which will use my face to unlock my bedroom door, like you’d unlock an iphone. My time spent learning and training image classification algorithms has not only equipped me with the technical skills to succeed at (company) but also provided me with a framework to do so responsibly and ethically, which are key points expressed in (company)’s mission statement.

Candidate with 1–3 years of experience

My name is (your name). I have a (degree) and I spent the past (# of years) at (company) where I developed ETL pipelines and scaled data infrastructure within the hospitality industry. Although I leveraged and gained proficiency with industry standard tools like Apache Airflow, Docker and AWS cloud infrastructure, more importantly, I had an opportunity to spend a week working in the field to better understand how our data served stakeholders, frontline workers and hotel guests. As (data role) we’re often confined to our projects and teams, but in the spirit of (company)’s mission statement, I do all I can to escape our (engineer/scientist/analyst/developer) bubble to truly understand business needs, an approach I’d bring to (company).

Both of these introductions thoroughly and concisely answer the “tell me about yourself” question while highlighting the candidate’s skills and specialties. But what they do even better is tell the interviewer how the candidate would apply those skills not only to a particular industry, but specifically to the business in question.

You might think it overkill to analyze company mission statements and pour over “About Us” webpages. But the research pays off when you’re able to craft specific, insightful answers like these. Not only are you able to answer your interviewer’s question, but confidence skyrockets when you see an interviewer smile or nod as they think: “This person gets it.”

By the way, I got that internship I applied for and spent fall of 2017 working at The Tonight Show Starring Jimmy Fallon.

To this day, that was the only interview I’ve left more confident than when I started. All thanks to prep and those Wall Street killers.