Product Case Interviews for Data Science Jobs: Dos and Don'ts Explained

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Product Case Interviews Dos And Don'ts | Produce Sense | Data Science Interviews
This video looks at a specific kind of interview for data scientists: product case interviews. I explore the do's and don'ts so that you know how best to prepare to crack these interviews.
My top 5 tips for product case interviews are:
- It's okay to make mistakes
- Quality is better than quantity
- Interact with the interviewer
- Always ask clarifying questions
- Ask the interviewer for time to structure your answer
The 4 red flags are:
- Not being able to answer at all
- Having too many ideas
- Following a framework blindly
- Not being able to defend yourself
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====================
Contents of this video:
====================
00:00 Topic of the video
00:50 It's okay to make mistakes
01:55 Quality is better than quantity
02:41 Interact with the interviewer
03:57 Always ask clarifying questions
06:26 Ask the interviewer for time to structure your answer
07:31 Not being able to answer at all
08:51 Having too many ideas
09:33 Following a framework blindly
10:21 Not being able to defend yourself
11:41 Summary

Опубликовано:

11 июл 2024

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Комментарии : 14

@emma_ding 2 года назад

If you prefer to read rather than watch this video, check out the blog post I wrote on this, and other, topics here ➡ www.emmading.com/resources

@dataseance4041 2 года назад

Such great tips, especially the "it's OK to make mistakes" and "don't shoe-horn a problem into a framework" parts. I quickly realized after a few interviews that making mistakes is inevitable; what makes or breaks an interview is whether you can recognize your mistakes and seek the right kind & amount of help from the interviewer so you can get back on track. Super stressful but also a fun and realistic process. I'm of two minds about frameworks, though. On the one hand, I find it hard to think thoroughly about what matters for a specific industry (e.g., social media, finance, multisided markets, etc.) without using a framework of some sort, but on the other, I agree 100% that thinking from first principles is key for data scientists. Curious how you go about striking a balance between being thorough vs. being specific to the problem.🤔

@eddom30 2 года назад

Your videos are really good!, Thanks for the tips

@relaxationmusic7094 2 года назад

Thank you so much. Really nice video.

@analyticsai3674 2 года назад

Superb Emma!

@turtlegangbigshells 2 года назад

Thank you! This came in perfect time. I have an onsite tomorrow :)

@rajjohri8547 2 года назад

how did it go

@collegebgi8167 Год назад

Emma 我通过了atlassian的first round product case 面试！我用了你的framework然后面试官说他很impressed！

@emma_ding Год назад

太棒啦！为你开心，加油！

@collegebgi8167 Год назад

@@emma_ding 谢谢你！

@analisamelojete1966 2 года назад

I do agree with many of these tips. Mostly because test cases are arbitrary and plain stupid as most of them want you to build an entire data science process, including app and data architecture with itself is stupid. If a company gives you a test case asking for data infrastructure and want you to be familiar with everything, just skip that company.

@michaelxie1978 2 года назад

Good content and presentation, sadly only 7.5K views. You need to do some promo...

@lecryptojames 2 года назад

I have a Data Engineer Case Study interview on Friday, so still helpful. Just need to reframe in regards to Data Infrastructure, Database Design, etc. based on what scenario I am given. Any other thoughts are much appreciated.

@lecryptojames 2 года назад

It should be something like, given the scenario where I am to design an ETL data pipeline, I need to first consider if I should process data on-premise or cloud. I would mention that I want to take full advantage of server less computing so that I would not have to deal with all of the hardware overhead issues, and find a more fault-tolerant, scalable data storage solution. Thus, I will integrate AWS services such as EMR as a data processing framework and DynamoDB as a storage solution. I will incorporate working with a Python script to run parallel processing. I will work with Terraform to automatically launch services based on desired features. To operationalize, I will work with Docker and Kubernetes...okay I think I need to study more on operationalizing data systems.