Data Science Product interview questions are known to be difficult questions in data science interviews, since these questions do not have a fixed solution. These product interview questions can become easier by practicing, and understanding the framework!
Data Science product interview questions can be split into 3 categories:
Metric related problems usually involve a certain feature/product that has decreased in a certain metric. When providing your solution, you should explain how you derived your solution and explain the root cause of the problem.
Examples of these types of questions:
Feature A has been decreased in use by X%
Uber asks “Uber Black rides have dropped 10%. How would you investigate this reduction?”
The company’s product has decreased in a certain metric by X%, but increased in another metric by Y%, i.e. Google asks “Google noted that there was an increase by 8% in users who mentioned their privacy was respected. On the other hand, engagement in Google Play Services has decreased by 14%. How would you determine the source of this irregularity?”
Companies look for:
2. Measuring impact
These questions are asked to understand how the interviewee can determine the success/failure of a product.
Examples of these types of questions:
Companies look for:
3. Designing a product
Designing product interview questions has no correct solution, but tests how well you provide evidence behind each step of your solution. These questions generally involve the company’s product or another company’s product that would help the company.
Examples of these types of questions:
Companies look for:
The solution to these types of product interview questions should be answered in different ways depending on the questions’ category, product/feature in question, and interviewer’s comments. An in-depth explanation on solving these types of questions is discussed in the “The Ultimate Guide to Product Data Science Interview Questions” article by StrataScratch.
Each question category has various examples, common pointers, and YouTube interview examples. The YouTube interview examples are important, since this explains how important the interviewer's comments are during the interview! While on paper two questions may seem similar, the assumptions and background to the questions will differ! The interviewer will provide more context to the question, so you can understand how to structure your solution. The YouTube interview videos, included under each section, are a simulated environment of product data science interviews questions between an interviewer and interviewee. Do check out these interviews to get more context on how to go about these questions, especially if you have not done an interview before!
Here is a list of practice questions classified into their respective category.
Pick one of the company’s products. You are now responsible for building a dashboard that will show all relevant metrics to the team daily. What do you track?
Metric questions are often related to change in the number of users. Snapchat (Question #4 above) asks if the user change means the total number of users or the number of unique users?
When a metric varies, the change may only occur in certain geographical locations. Using the Snapchat example, the decrease in users may be due to a certain location. Ask the interviewer if the number of users has decreased in any certain geographical location and if it has you need to go about the solution in a different route.
When a specific location/date is mentioned, you need to mention how this location/date is related to the product. For example YouTube’s example (#2) A reason for this could be a significant event that occurred the previous day which is important to Indonesians could have been uploaded to YouTube. This would cause a sudden increase in unique YouTube users.
An example for the date mentioned could be Yammer’s example (#5). There could be a business trend related to photos or significant events in October.
Since measuring impact questions are similar, pick any one of the company’s products and understand how data scientists at the company will measure the success of a product/feature. Find metrics or features that are unique to the company’s product and try using this in your answer to show you have a better understanding of the company and its products.
Most of the time when a company mentions to measure the impact of a feature, people usually mention A/B testing and think that is good enough. Most companies do not accept this answer and would mention due to certain factors we can not A/B test the feature.
A key pointer that people forget for measuring impact questions is not the metrics measuring success, but the metrics that do not worsen in the release of a new product. Using the Instagram question (#8) as an example, while you can mention clickthrough rate as a measurement of success, you should mention at least one metric that should not decrease, such as organic growth rate.
Don’t forget when mentioning metrics, explain how these metrics are directly related to the impact of a product.
Pick one of the company’s products/features. You have to describe how you would improve this product/feature.
Product design interview questions are extremely diverse and hard to predict. Common concepts are based on how you will design a product with some quantification. For example with the Twitter example (Question #3), you will need to understand how Twitter followers can interact with a certain user. For example Engagement from followers can be quantified through likes, comments, and retweets. You can take this further by conducting sentiment analysis on the retweets. If a lot of retweets are positive, then it can be measured that the user has a higher influence on other users. These product interview questions require you to dissect the specifics of the question and see how well you understand the product.
Another key factor is how will this designed product benefit the company. While some products, such as the Lyft example (Question #4) are straightforward on how it would benefit the company, the Microsoft example (Question #2) is not as direct as to why Microsoft wants to summarize a Twitter feed. Understand why the company would want to understand the development process of the company. Possibly Microsoft wants to see what people feel about a newly released feature/product. Think about how your solution to the question would help the company to develop its product.
This YouTube video provides insight into answering metrics and success measuring questions. While this video does contain other explanations to questions such as opportunity sizing, this is not required for Data Science product sense interviews, so you could skip those parts!
A lot of product designing data science interview questions overlap with product manager questions. There are two things to keep in mind when using product manager questions as practice. Product manager questions often go much more in-depth than required by product design data science interview responses. For example, PMs may require you to explain a go-to-market strategy, but this is not required for Data Science interview questions. Product manager questions may also involve reporting decisions to executives. This would be the summary of your answer.
Whenever you are researching a new product or going through a product, try to create your product questions! Understand why a company would choose/not choose to implement a certain feature. Companies have probably already thought about your reasoning on why they did not implement a certain feature/product. Question why they would have done that!
Also published on StrataScratch.