At Interview Query, we’ve bucketed these interviews into five distinct categories. Some get re-worded, so they’re trickier. Some are repeated almost word for word from the examples we’ve found. Here are summaries of each type of interview question before we dive into the course structure below.

Investigate metrics questions

This is the most common type of product interview question. These questions are based on a certain metric going up or down and how this metric may communicate the health of a product. One common question asks the candidate to determine why feature X, for instance, is dropping by Y percent.

For example: “Why are Facebook friend requests dropping by 10%?”

With this question, we’d want to know information about the drop– is it important, does it affect the business, and what may be the cause? An additional addendum to this question is on fractional metrics, which play into the concept of analyzing a metric that is a fraction.

Investigate fractional metrics

A subset of investigating metric questions deals specifically with fractional metrics. These can be metrics like the percentage of active users (since that’s active users/total users) or average views per influencer. One must pay special attention to carefully defining the numerator and denominator of such metrics.

Measure success questions

The second most common product question is about measuring success. These questions ask for methods to measure the success of the feature or product. Examples could be: “How would we measure the success of the Facebook marketplace?” or “How would we measure the success of Yelp reviews?”

These are all features of an overlying platform or product, which ties into the product roadmap timeline we referenced above to analyze the success of released features. The company wants to know if the feature has improved anything and if it was worth the time and effort to build. At this point, the data scientist steps in and thinks of a methodology to investigate the data and determine its success.

Feature change questions

The third type is feature changes. A typical question is in the format, “Let’s say we want to add/change/improve a new feature to product X. What metrics would we track to make sure it’s a good idea?”

At first glance, this may seem similar to the previous question–we would track a feature change and its metrics. The small difference of a feature change is that the variables at play also changes. Instead of analyzing the entire product, we’re tasked with analyzing how this feature may have affected users.

Metric trade-off questions

The fourth type of question is about metric trade-offs. This question can be along the lines of: “If you are the product manager for Facebook and you see that comments are down by 10%, yet reactions are up by 15%, how would you deal with it?”

This question is extremely common, especially on huge platforms such as Facebook, Google, and Amazon. There are definite causal effects on other features when the site updates or new features are released. A data scientist in this situation would need to decide which metrics matter, why this change might have occurred, and tackle the severity of the change.

Growth questions

The last type of question, which may not be as common as the other four, is on growth. A possible question could be: “We want to grow X metric on Y feature; how would we do that?”

Primarily, only companies focused on growth would utilize this problem. Thus, this question may appear in interviews for startups in growth mode or for companies with a large acquisition team.

Closing notes

Before we move on, we should note that these companies want to gain a sense of our strategic skill and business intuition in tackling these questions. One piece of advice is to think big and always take a step back before you dive in. A common mistake is to dive in and get pigeonholed into one specific idea. If we concentrate too much on a specific area, it’s difficult to envision the full cycle of the product or our ultimate product goal.