A year out of college, I interviewed for a data science role at LinkedIn. I remember being asked a product data science interview question. The interviewer asked me how I would determine if a new feature on LinkedIn messaging would affect user experience.

I was confident in telling them we could AB test this new feature. However, they responded by saying that they didn’t have the capacity to do that. So, what then?

I froze because, honestly, I had no idea.

No one is good at product interviews unless they’ve been through them before. They comprise specific types of interview questions that are meant to test your ability to understand how to build products. Given the challenging nature of interpreting a case question in under an hour, it is especially tough to learn how to answer them.

This specific type of interview is increasingly common for analytical roles. Big tech companies, such as Facebook and Google, mainly focus on product metrics and analytics. Here, data scientists function as an intersection between product managers, analysts, and software engineers. Data science, by definition, is a combination of product management and software engineering.

Next, let’s dive into how to solve product interview questions.

Intended audience

This course is curated for anyone interested in learning data science and getting the skills necessary to ace data science, machine learning, or product analyst interviews.

A product interview is vital in every technical interview and is a commonly asked question at tech companies. Being able to answer product interview questions shows our ability to determine the problem a particular product has, what we should investigate to improve a product, and how we can measure the success of such a product.

This course will help us understand the basics of product interviews and provide the necessary practice on how to approach each area of a product or feature that will be helpful for us during product interviews.