Cracking Facebook (Meta) Product Case Interviews: Tips for Data Science Interview Success!

Facebook/Twitter Metric Interview Question and Answer | Data Science Interview | How to Improve a Product
In this video, we'll go over how to answer one of the most challenging metric/product sense/case questions in data science interviews - how to improve a product.
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Contents of this video:
====================
0:00 Intro
2:27 Clarifying the question
4:55 Product improvement ideas - User journey map
8:23 Product improvement ideas - User segmentation
13:12 Prioritize ideas
14:52 Measure success
15:42 Summary
At 12:28, "launch a complain" should be "launch a campaign".

[@jasonwong8315]

love the content you share and love the way you talk!

[@abtestingvideos2259]

Never seen any video that has similar level of clarity and insightfulness. Thanks so much Emma for your generosity to share your knowledge!

[@dataseance4041]

The brainstorming part is gold! I just started preparing for product sense interviews and found generating ideas (e.g., to improve or "debug" a product/feature) to be by far the most challenging step yet people rarely give concrete examples. Kudos to you for doing that in your video!

[@GalaxyZhou1006]

very useful tips, with your previous product sense frame work as basis, I established ]more targeted/detail-oriented frameworks towards the role I was applying for and finally passed the final rounds of a tech company :) just wanted to let you know your video really helped and please keep on with the great work!

[@songxiyou2347]

Another very nice video! I do agree that the "launch or not" is the most difficult product sense problem. But I think I will do better with the help from Emma!

[@kunkung8367]

This is the first time ever I came to understand what exactly the interviewer was looking for by 'brainstorm some ideas' - by no means were they expecting random/whimsical ideas, but rather it's an ask for structured/systematic thinking. Great stuff as always, thanks Emma!

[@matthewdong9368]

THIS IS GOLD!!!!! Thank you so much,谢谢！！！

[@tselmeguriankhai5725]

Thanks, Emma, it is very helpful!

[@ravikumarsrivastava8442]

Awesome video! Very very helpful. Appreciate you for creating this!

[@cming6108]

Insightful. Favorite Channel

[@dunjianxiao4105]

Thank you very much. Very Informative. 属实宝藏了！

[@josephtran1500]

Thank you for posting. Great content. Just subscribed.

[@SparshAgarwal]

Thank you. It was a very insightful. The most informative video on this topic I have seen.

[@imdeepu7855]

This is one of the best video that i ever seen related to Product case study, absolutely crystal clear explanation.

[@xinyuli1870]

Thank you so much for sharing! It is a great video to get inspiration. Could you post more data science interview prep related to Facebook, please? cannot hearing more.

[@jeoffleonora4612]

Great video. I watched 3 times already:)

[@weiyang2116]

Please keep the videos going! Subscribed! ;)

[@yuthpatirathi2719]

This is super helpful emma . Thank you ! you are a treasure

[@naveengoel007]

I would highly recommend this video, Very nicely created

[@jacobmoore8734]

I just signed a DS offer with Fb yesterday. No joke. This video seriously helped! The product case interviews were by hard the hardest of the 4 sessions in "the loop". Thanks for creating this content!

[@aditimulye9698]

Hi Jacob, I have just received an email from a University Recruiter at Facebook. May I get in touch with you for a bit of guidance and insights? I've never given a case interview before this.

[@clapdrix72]

Cool man! Just be aware that a FB DS Product is actually just an analyst so if you try to get ML based jobs in the future it could be difficult because this is well known. That is why my colleagues and I in the DS dept at EA don't respond to FB recruiters.

[@jacobmoore8734]

@@clapdrix72 I want to grow as a statistician, not an ML engineer. So no issues here. Plus I had an NLP heavy ML eng role and I left because I just didn’t find using BERT every day to be all that emotionally rewarding. Using data to drive strategy is more fun for me personally. Different strokes! :)

[@clapdrix72]

@@jacobmoore8734 Cheers!

[@amrmahmoud3799]

@@jacobmoore8734 nice Jacob, this is what I did I left ML and focused on data

[@salmam9373]

Thank you! This is so helpful

[@deejohn2242]

Wow, your video is so informative and reality. Thank you so much!

[@abdullahadam8219]

Awesome video!

[@reneli8958]

Awesome video to break down the thinking process

[@oliver5356]

This is really good content. Thankyou!

[@thampasaurusrex3716]

Thank u Emma!

[@ricardolee5597]

Please keep this up. It's super helpful for interview prep but also great to inspire me on how to be a better analytics professional

[@emma_ding]

Will do, thanks for the feedback!

[@SuperLOLABC]

Great Video!

[@EvanZamir]

Great video!

[@yitongsun6702]

So helpful video I've never seen about products question

[@NN-up5fm]

This is Gold, thanks for the videos. I like the breakdown/structure of your content. Can you please do videos on Data Engineer interview(perhaps for facebook) as well, along the lines of Data Modeling, ETL and Product sense ?

[@emma_ding]

Will definitely put it on my list of content ideas!

[@AlirezaAminian]

Very informative video. I like the fact that she provides multiple examples of all the items and subjects that she mentions.

[@emma_ding]

Thank you for your kind feedback, Alireza! 😊

[@mussdroid]

Climbing mount everest of Data Science with Emma , I love data Science after start watching Emma videos , I would improve whatsapp by adding whatsapp pay ! 💃🏻🙏🏻🚀✅

[@shrutis.tiwari8335]

Hey Emma, I am following your videos from the beginning and I find your method of approaching a problem is very organized, I also got inspired by your back story when you prepared for the interviews looking for job and eventually turned towards becoming interview coach. Kudos to you! I have a question, can you please share some resources on product data science, not just for interviews but for improving skills. Thanks in advance and keep up the good work.

[@emma_ding]

Hi, Shruti! 👋 Here's a blog post I wrote that illustrates the various data science roles, with charts detailing the most integral skills for each position: www.emmading.com/blog/interviewing-for-your-first-data-scientist-job-what-to-expect-and-how-to-prepare Additionally, feel free to check out the "Resources" tab on my website once you decide which skillset you'd like to begin tackling first! I hope this helps. 💛

[@Aut0KAD]

these are great videos

[@ziqimeng592]

Thanks Emma for the video. Really helpful! if the interview question is pretty generic like how to improve facebook user engagement? can we just pick one feature to talk about? like we can just talk about how to improve the feature what's on your mind . or we should come up with some new features that could be added to Facebook?

[@johnpark3680]

Great content! About the frame work, how about we first look at user journey data to understand what part the feature is not doing very well before brainstorming the idea? I feel this way it's easier to find user pain points. Thank you

[@emma_ding]

For sure! I'd suggest choosing an order that makes most sense to you. The answer I provided is just one of many possible answers.

[@ceciliaxu]

I am going to have an interview with Amazon in three weeks. I'm so glad that I found your channel and this video is really helpful. I will continue to watch the rest of them. Thank you!

[@danipills]

How it was? I have an interview tomorrow 🤓

[@jingsong5820]

Nice!

[@mahdimerced]

Hi It was a good video. Be sure to leave more videos from airbnb to get to know more

[@janicejiang5247]

great video, very structured. Thank you Emma! P.S it is possible to schedule a mock product sense interview with you?

[@emma_ding]

You can fill out this form data-interview-questions.web.app/ and let me know your past interview experiences. We can go from there.

[@janicejiang5247]

@@emma_ding lovely! Just filled out and I look forward to hearing back from you soon

[@DrEhrfurchtgebietend]

Could listen to you all day

[@emma_ding]

Thank you Dr. Ehrfurchtgebietend! That is very kind of you to say!

[@Amanda-zn3su]

Great Video! any chance you can also do a video on "how to talk about a data science project in an interview"?

[@emma_ding]

Good idea! Do you mean how to describe a project in an interview (such as an experience interview with a hiring manager) or how to do a presentation on a project? Are there any specific questions you have in mind? Would tips shared in this post towardsdatascience.com/how-i-got-4-data-science-offers-and-doubled-my-income-2-months-after-being-laid-off-b3b6d2de6938#6f86 be enough?

[@Amanda-zn3su]

@@emma_ding thanks so much for getting back & sharing the article! I was referring more to the first one you mentioned above, i like the tips in the medium post, but would like more examples? (sometimes i dont know how much detail i need to go into when talking about a project i.e. a fundamental data science research)

[@emma_ding]

Hey Ailing, I just published a video on this topic kzbin.info/www/bejne/poe0XmNrj76NgKc, hope it helps! Let me know if you have any comment :)

[@dmatekenya1]

@@emma_ding do you know any platform for doing mock interviews?

[@hhliu7287]

Very helpful! The clarify questions are very obvious，what if I already know that every user can use it and user needs to log in to use it. what else can I ask to clarify? If you can share the word doc of your bullet point will be very helpful, I have to screen shot the bullet point.

[@airliu1]

15:00 - Success Metrics... was expecting more content on metrics or KPIs in this video.

[@mr.y981]

Very informative. One question: if I had a project from a website such as udacity data analysis projects, should I mention that project or is it better to only focus on real life projects?

[@emma_ding]

If you have work or real life projects, you can prioritize those. Otherwise, you can mention class projects and Udacity projects.

[@florazhang413]

Hi Emma, thank you so much for sharing these videos. Your videos are really helpful and I have learned a lot from them. At 12:28, you mentioned, "launch a complain". Do you mean "launch a campaign"?

[@emma_ding]

Yes, good catch! Thank you Flora Zhang!

[@Bestofsumprobabietc]

@@emma_ding q0

[@minjeongkim986]

i will have case interview, which is about traditional-consulting style and data oriented business problem. So in this case, do i not need to provide ML models?

[@mijikim7115]

Great! Thanks. is there a link for your slides?

[@emma_ding]

Sorry there are no slides. It's part of the video editing.

[@syafiqashraff5770]

Great content, learnt a lot thanks. BTW you're beautiful

[@borisguo4663]

What is difference of engagement and retention

[@tejas5872]

Hi Emma, I'm from mechanical engineering background so Im having a tough time making a list of coding problems from leetcode, hackerrank etc that might be asked in interview because of I'm not aware of nuances. I am good at python programming, Could you please help me with this? Thank you.

[@emma_ding]

This blog summarizes all the different kinds of coding interviews and I think it may clarify things towardsdatascience.com/the-ultimate-guide-to-acing-coding-interviews-for-data-scientists-d45c99d6bddc!

[@qinjun76]

12:26 I think you meant campaign instead of complain. Nevertheless, it is a great job.

[@emma_ding]

Thanks for pointing out the typo! I've put it in the video description.

[@qinjun76]

@@emma_ding I really love your videos! Very well organized and to the point!

[@observer698]

15:31 what do you mean by 2 sided market?

[@emma_ding]

Hey great question! I would recommend checking out this blog post if you want to know more about what exactly I mean by a 2 sided market: medium.com/sequoia-capital/two-sided-marketplaces-and-engagement-ded7d5dcfe71

[@beyondtheclouds95]

a pie chart?! come on emma

[@emma_ding]

Hey, I'm interested to know if you have better ideas! :)

[@dmatekenya1]

How can I contact you? I’m wondering if you would be willing to mock interview me? I’m preparing for on-site interview for Google and FB

[@ishitasadhukhan1]

Hi Dunstan , may I please contact with you for your insights and guidance for upcoming Product Sense Interview at Meta? Thank you so much !

[@ASURAN24]

12:26 Launch a campaign. You had a typo.

[@apurvgarg6590]

This is more of a framework for Product Manager interviews. As a Data Scientist, we expect you to be more focused on data.

[@emma_ding]

I'm interested in learning more. Could you please be more specific on this suggestion?

[@leisana4097]

@@emma_ding Most likely this guy is referring to the machine learning model building steps. Like data preprocessing, model building, training , evaluation and deployment. These are typical text book knowledge type questions e.g A specific question would be how do you deal with missing data. Or it could be something like what's the difference between gradient descent and stochastic gradient descent. These type of questions are necessary to know but when it comes to actual scenario based questions these text book type questions will not help much in connecting the dots and giving a step by step systematic approach to most interview questions. Anyways, I think, you have put up nice step by step approach in forming a solution to a problem which can be used in any domain. It's really important to know the underlying goal of what we are doing and formulate the answers in a systematic way which you have addressed nicely. Thanks.

[@leisana4097]

@Apurv Garg - Can you please read the title of the video. The title clearly says what this topic is all about. If you are interested in data specific questions of data science there are plenty of videos describing those and choose to one which fits your problem.

[@arunvaibhav5059]

The light on your face is annoyingly bright!! Great info though.. appreciate it!