The ultimate guide to product analytics interviews. Strategies developed by FAANG engineers to craft structured, impactful answers and increase your chances of landing a data science job.

Product analytics interviews evaluate your skills and creativity as an aspiring data analyst. Interview questions evaluate if you can use your technical knowledge to guide the further development of a product. Learning how to answer these questions is crucial to ace most job interviews for data scientists and analysts.

In this course, you’ll first gain an overview of what product analytics interviews look like and what kinds of questions usually come up. Next, you’ll learn to use a framework for answering product analytics questions, showcasing your analytical skills. Finally, you’ll learn how to apply said framework to different kinds of product analytics interview questions by looking at several examples.

With this course, you’ll increase your chances of landing a job in data science by learning how to effectively answer product analytics interview questions. You’ll be able to provide well-structured and well-communicated answers, and show you can creatively tackle any business problem you may face.

Ace the Product Analytics Job Interview

Once we have defined most of our background context, set assumptions, clarified the question, understood the product, and set top-level metrics, we will move on to what is generally considered the hardest part: applying business intuition to the product and doing more in-depth analyses.

There are generally two main parts to the analysis of success which we are trying:

In this part, it's extremely important to be able to understand how our product goals relate back to the metrics that we are tracking. The top-line metrics that we discuss are surface level to understanding the health and growth of the product from the standpoint that we don't want to see large drops in metrics, like total events created and users accepting event invites.

# Engagement
If we prioritize engagement metrics long-term, we have to pick a few metrics that will showcase how the product is empowering engagement within the broader platform. In many cases, this means examining how product features contribute to "success" in engagement.

We can define success in engagement within users in a few different ways. For most companies with a network effect, this can be the creation of more posts and comments than can be drawn in from continuous engagement with other users' content on its platform. Over the long term, our main metrics to measure are increasing engagement and long-term retention.

**Long-term retention** is the measurement of how the product or feature helps contribute to the continued use of the platform as a whole. For Facebook Events, this would then be a question of how Facebook Events contributes to users engaging and being retained on Facebook over time.

## Example answer


Differentiate between the three main goals that a product can have. 

Introduction

Investigating Metrics

Fractional Metrics

Measuring Success

Feature Change

Metric Trade-Offs

Growth

Conclusion

Prioritize Metrics and Test Hypothesis