Mindless Interview Questions From Real Data Science Job Interviews

Ambiguity into Opportunity: Strategies for Open-Ended Data Science Questions

Adam Ross Nelson
14 min readDec 23, 2023

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As you read below you might think, are these questions for real? And, I promise you, they are.

Embarking on a job search in the field of data science can be a journey filled with mixed emotions. For many mid- and late-career professionals, including those in data science, machine learning, artificial intelligence, or advanced analytics, facing a job interview can be particularly daunting.

It’s not always the complexity of technical questions that poses the biggest challenge, but rather the simplicity and generality of some queries. These questions, often lacking in specificity, can leave even the most established professionals perplexed and searching for an appropriate response.

Three data science bootcamp classrooms with students at desks.
Image Credit: Author’s illustration created in Canva.

As a career coach and data science bootcamp instructor, I have a unique vantage point on the intersection of career development and technical expertise in the field of data science. Every day, I engage with established professionals who are either entering or leveling up in the world of data science, machine learning, artificial intelligence, or advanced analytics. This exposure grants me insight into the various challenges and opportunities that arise during job interviews in this dynamic field.

In this article, I explore some of these mind-boggling interview questions. I give examples of such questions. I discuss why they can be tricky to answer. And I offer insights on how to effectively respond to them. Remember, everyone has to start someplace, and understanding these questions is a step forward in your career transition or advancement.

Through my interactions with both job candidates and hiring professionals, I’ve accumulated a wealth of real-world interview questions — some intriguing, others bewilderingly generic.

It’s these latter questions, often broad and non-specific, that can unexpectedly stump even the most well-prepared data science professionals. They are not challenging because they delve into obscure methods or complex code…

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Adam Ross Nelson

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