A General Framework for Tackling Product Questions

Product questions are purposely ambiguous and open-ended since interviewers test how interviewees react to such questions. In a real-life work setting, we won’t have black-and-white answers ready for every problem, so learning what to do early on is incredibly important.

There are a couple of different frameworks available for tackling product interview questions. We’ve combined these frameworks with an additional focus on metrics and applied data that employers expect from data scientist candidates. This framework will guide the structure of this course.

While product managers cover a decent amount of this in their interviews, data scientists can focus less on the end-user experience and more on larger behavioral changes from their users. A good, general framework includes the following:

1. Clarifying the question

During an interview, we almost always start with an information disadvantage. That’s why we should ask clarifying questions. What are the product goals? What’s the background context? What questions do we need to ask to bridge the gap?

2. Make assumptions

Make some assumptions about the problem to narrow the scope. State what you’ll explore in your analysis and what you won’t.

3. Analyze user flows

Examine exactly how the product works. How does a user get to a certain feature? How does a user use a certain feature? What kinds of different users are there?

4. Define the hypothesis

To determine the root cause of an issue, we must first hypothesize the situation and explore other possible scenarios.

5. Draw metrics to support your hypothesis

Use metrics to illustrate further how they could prove or disprove our hypothesis.

6. Tie your analysis to the product goals

Finally, tie the analysis back to the product goals. Give some summary statement that can prioritize which ones matter and what the next steps are.