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

## Clarification

Common things to consider within the clarification step include:

**Timeframe:** Is this a one-time event? Is this a progressive event?

**Metric definition**: Are these unique metrics or total metrics? For example, are we looking at unique applicants per job or total applicants per job?

**Decrease/increase in percentage**: How big is the decrease or increase in the metric?

The clarification step here allows us to map out exactly which direction we’re going to take our investigation. If it can’t be ascertained from the prompt, we’re going to have to go through both (or all) scenarios and consider why each may be happening.

A common and important metric to consider would be the unique number. Let’s say that we know the total number of applicants is going down and the unique number of applicants has also decreased. This gives us more information than just the former, allowing us to form conclusions about where the decrease occurred.

## Gathering context

The question already states the numerator and denominator: “applicants per day” has been decreasing. Here, the numerator would be the number of applicants, while the denominator would include the number of job postings. Given that metric, knowing that it decreases offers us two possibilities:

1. The numerator has gotten smaller (fewer total applicants)
2. The denominator has gotten bigger (more jobs on the platform)


For each one of these interviews, it’s important to state the effects of the numerator vs. denominator upfront. This way, the interviewer can direct us down the right path.

For example, let’s propose that the number of job postings has more or less remained the same, but the number of applicants is steadily decreasing. Therefore, we can clarify two things: it is a progressive decline, and the decline is coming from the numerator getting smaller (the number of applicants decreasing).

Note that if we consider the unique number of applicants in this scenario by corresponding it with the unique number of job postings, it’s a different application than that described above for applicants, as job postings are inherently unique and therefore yield a many-to-one relationship.

After we state one scenario,  the interviewer will ask us why this could be the case. We could then work our way through the investigating metrics framework. After that, they might  say: “Well, what if there are actually more jobs on the platform? What do we think could be happening then?”

Be ready to explore either scenario as, ultimately, this is a technical conversation where the interviewer is trying to understand what we’re thinking.

# Clarification

Common things to consider within the clarification step include:

**Timeframe:** Is this a one-time event? Is this a progressive event?

**Metric definition**: Are these unique metrics or total metrics? For example, are we looking at unique applicants per job or total applicants per job?

**Decrease/increase in percentage**: How big is the decrease or increase in the metric?

The clarification step here allows us to map out exactly which direction we’re going to take our investigation. If it can’t be ascertained from the prompt, we’re going to have to go through both (or all) scenarios and consider why each may be happening.

A common and important metric to consider would be the unique number. Let’s say that we know the total number of applicants is going down and the unique number of applicants has also decreased. This gives us more information than just the former, allowing us to form conclusions about where the decrease occurred.

# Gathering context

The question already states the numerator and denominator: “applicants per day” has been decreasing. Here, the numerator would be the number of applicants, while the denominator would include the number of job postings. Given that metric, knowing that it decreases offers us two possibilities:

1. The numerator has gotten smaller (fewer total applicants)
2. The denominator has gotten bigger (more jobs on the platform)


For each one of these interviews, it’s important to state the effects of the numerator vs. denominator upfront. This way, the interviewer can direct us down the right path.

For example, let’s propose that the number of job postings has more or less remained the same, but the number of applicants is steadily decreasing. Therefore, we can clarify two things: it is a progressive decline, and the decline is coming from the numerator getting smaller (the number of applicants decreasing).

Note that if we consider the unique number of applicants in this scenario by corresponding it with the unique number of job postings, it’s a different application than that described above for applicants, as job postings are inherently unique and therefore yield a many-to-one relationship.

After we state one scenario,  the interviewer will ask us why this could be the case. We could then work our way through the investigating metrics framework. After that, they might  say: “Well, what if there are actually more jobs on the platform? What do we think could be happening then?”

Be ready to explore either scenario as, ultimately, this is a technical conversation where the interviewer is trying to understand what we’re thinking.

Use our framework and review the considerations for the fractional metrics.

Introduction

Investigating Metrics

Fractional Metrics

Measuring Success

Feature Change

Metric Trade-Offs

Growth

Conclusion

Declining Applicants Example

Clarification