Usha Bhanu Komaragiri — Published On June 28, 2022
Beginner Career Data Science Interviews

This article was published as a part of the Data Science Blogathon.

Data Science Interview

Image 1

Introduction to Data Science Interview Case Study

When asked about a business case challenge at an interview for a Machine learning engineer, Data scientist, or other comparable position, it is typical to become nervous. Top firms like FAANG like to integrate business case problems in their screening process these days. This approach is followed by a few other leading companies, like Uber and Twitter. Most case studies are open-minded and technical. They are specific to the company you are interviewing for.

 

What is a business case problem in a data science interview?

In basic terms, a case study is any real-world project you may work on in your company. The type of case study question you’ll be asked is determined by the position you’re interviewing for. In most interviews, you will be given around 45 minutes to absorb the problem description and walk through your thoughts and potential solutions.

“If you cannot measure it, you cannot improve it.”

                                                                                   Peter Drucker

Getting the right metrics for any case study is important. It can be difficult due to different reasons like being a fresher, or having no knowledge of the industry.

Note: A business case problem does not have a single correct answer or a simple solution.

Mainly case studies can be categorized into three types. They are Product based case studies, prediction-based case studies (Machine learning), and business case studies.

Example of a Data Science Interview Case Study

Pretend you’re interviewing for a position on Twitter’s engagement team. Twitter does have a news feed, as we all know. It provides the content to the user based on their interests. With the assistance of a news feed ranking algorithm, it can do this task. As a part of the engagement team, you are assigned a task to evaluate the algorithm’s success.

Data Science Interview                                                                                                                                                      Image 2

Framework

When you’re given a problem, never begin by firing your techniques; instead, start by clarifying the case study. Make absolutely sure the interviewer and you are on the same page. Because the questions tend to be confusing and indefinite! Asking questions will help you get answers or extra information. Show you are curious about it. As the case studies are open-minded avoid words like “correct approach is/ would be” because there might be multiple solutions to that problem statement.

Begin with a phrase like “Before going into the problem, I’d like to double-check that I understood the problem here…….”. Keeping track of your approach will be much easier if you take notes. It’s absolutely OK to pause for a while to consider and plan before proceeding with the solutions.

Then, to demonstrate that you understood the problem, concentrate on what actually is the problem; for example, in our study, the problem is focused on Twitter’s news feed and user engagement. As a result, the algorithm’s success is evaluated.

The first step is to identify relevant indicators to assess, such as shares, comments, and click-through rates (CTR). The strategy you choose to address the problem is the next step. Questions like these might be included “I’m also thinking that these measurements don’t always follow a trend. Some may be rising, while others may be falling “. In such circumstances, you may pick your metrics based on your approach’s perspective, such as whether you want to focus on business or user experience. Consider ads from a business standpoint. The CTR is a powerful statistic. Because not all users share the content, a powerful metric for user experience is comments or reactions (for example, text processing of comments).

Take a step back and ask if the interviewer has any questions for you or if you can proceed. When you’ve completed the key phases of the process, be sure to cover the following:

1. Clarifying

2. Brainstorming and

3. Strategy

4. Conclusion

Make certain that your approach to the case study is complete. Also, discuss the results of your method in regard to the problem statement.

Additional Points to keep in Consideration

1. Response should be in a structured manner.

2. Conclude the case study with end to end solution.

3. A sensible Strategy.

4. Know your target audience.

You are stuck?

Don’t panic, or say irrelevant points, instead tell your interviewer that you are stuck. Let them know What you’re thinking and why you could not proceed with those approaches. In a few cases, the interviewer might give clues to you.

Wrong point?

When you are sure that you are leading towards a solution that might not work! or lead to a dead end. Don’t hesitate to mention that this might lead to a situation. Instead, concentrate on how would you correct and continue your strategy for the problem you have encountered. In real-life projects, you are expected to identify those problems and continue with an appropriate approach. You could add how your new approach to the problem will be avoiding the problem that you were stuck with.

Not sure about the Strategy?

Let the interviewer know that you are not sure about the approach and think out loud to get on the track. It is always better than bluffing.

Practice with yourself!

Ask yourself a few case studies! Record yourself on camera to observe and correct yourself. It helps you with gaining confidence before practicing with peers. With each practice, you will get better. Making notes of all your practice studies will help to refer in the future.

A resource I found useful to practice case study: https://www.interviewquery.com/blog-data-science-case-study-interview

Conclusion

Voila! Follow these four steps for a successful case study response ( Clarify, Plan, Strategy, and Conclusion).In other words, one can memorize the approach to a case study as “CAPER”.      Where C- Clarify, A- Assume, P- Plan, E- Execute, R- Review.

Make the interview as participatory as possible. Taking notes will benefit you in structuring your response. Mention the pitfalls to let them know both pros and cons of your chosen approach. Make sure you know all there is to know about the company and its products. Practice with peers and do mock interviews. Don’t worry if your first few efforts are a mess! Take the feedback seriously. I hope you liked my article on data science interview case study. Please share your feedback in the comments section below.

Image1:https://mockinterview.co/index.php/2018/02/26/four-sample-case-studies-for-data-scientists-analytics-positions/

Image2:https://www.searchenginejournal.com/increase-google-search-visibility-twitter/367324/

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

About the Author

Usha Bhanu Komaragiri

Our Top Authors

Download Analytics Vidhya App for the Latest blog/Article

Leave a Reply Your email address will not be published. Required fields are marked *