A Collection of Data Science Take-Home Challenges
1) How would you improve engagement on FB?
2) We are launching a new driver app with a better UI. The goal is increasing driver earnings by increasing their number of trips. Outline a testing strategy to see if the new app is better than the old one
3) Give an example of a case in which you run an A/B test, test wins with significant p-value, but you still choose to not make the change
4) Do you expect that Uber trips without rider review have been better, worse, or same as trips with reviews?
5) A given category of an e-commerce marketplace, for instance jeans, is not doing well. How would you estimate if it is a demand or supply problem?
6) What are the drawbacks of using supervised machine learning to predict frauds?
7) If 70% of Facebook users on iOS use Instagram, but only 35% of Facebook users on Android use Instagram, how would you investigate the discrepancy?
8) We made a change to our subscription offering adding new features. We expect this to increase subscription retention. How can we test if the change is successful?
9) Do you think it is better to target ads based on user demographic or behavioral characteristics (past browsing experience)? Why?
10) We ran an A/B test. Test won, so we make the change on the site for all users. But after waiting for some time, we realize that the new version of the site is not performing better than the old one. What could be the reason?
11) You are launching a messaging app. Define 2 metrics that you'd choose to monitor app performance during the first months. Why you chose them?
12) Which feature would you add to WhatsApp? Hypothetically, imagine you have access to all WhatsApp-related data, such as messages and where people click on the app
13) You have to predict conversion rate on Airbnb using user country as one of the input variables. How would you deal with the missing values in "country"?
14) How to estimate the value of a user coming to your e-commerce store when they land on your home-page for the first time?
15) How can we tell if two users on Facebook are best friends?
16) Which variables are important to predict a fake listing on eBay?
17) Explain the drawbacks of running an A/B test by market (i.e. all people in one market get version A of the site and another market version B)
18) How would you measure the performance of the customer service department?
19) FB - Should we add a love button?
20) How would you use data to evaluate if it makes sense to implement two-step authentication when users log in?
21) At Facebook we use as a metric number of likes per user per week. And, each week, we check it year over year to control for seasonality. This week the metric is dramatically down. How would you find out the reason? Logging is fine as well as the query we used to get the data
22) We are running 30 tests at the same time, trying different versions of our home page. In only one case test wins against old home page. P-value is 0.04. Would you make the change?
23) Each user on our site can be described by 100 continuous variables. What's the probability that a user is an outlier on at least one variable? What are the implications of this from a product standpoint?
24) LinkedIn has tested a new UI with the goal to increase the number of likes per user. They test it by giving the new UI to a random subset of users. Test wins by 5% on the target metric. What do you expect to happen after the new UI is applied to all users? Will that metric actually go up by ~5%, more, or less? Assume there is no novelty effect here
25) Describe one example of a classification problem where the cost of a false positive is way higher than false negative as well as the other way round
26) How to calculate for how long I should run an A/B test?
27) Suddenly, our dashboard shows that the number of picture uploads per day by Internet Explorer users went to zero. What could be the reason?
28) LinkedIn has launched its first version of the People You May Know Feature. How would you isolate the impact of the algorithm behind it w/o considering the UI change effect?
29) How would you find out if someone put a fake school on LinkedIn? I.e. they actually didn't attend it
30) You are supposed to run an A/B test for 3 weeks based on sample size calculation. But after 1 week, p-value is already significant with test winning. So your product manager pressures you to stop the test and declare it a winner. What would you tell her? Explain in layman's terms
31) What are the issues with splitting a small dataset (<1K events) in training/test set? What would you do then?
32) Using LinkedIn data, how would you predict when someone is going to change job? Assume you can use all LinkedIn user activity data
33) Between the following two metrics, which one would you choose to measure response time of an inquiry at Airbnb: percentage of responses within 16 hrs or average response time considering only responses within 16 hrs?
34) At FB, we found out that users with filled out profile infos (age, hometown, etc.) are more engaged than those without. Therefore, we figure out a way to fill out those infos automatically for all users hoping it would improve engagement. However, engagement barely changes. Why?
35) You ran an A/B test last year and it lost. When would it make sense to re-run the same test today?
36) How would you identify if an advertiser is using clickbait techniques without having a dataset with labeled events?
37) What are the most important parameters in a Random Forest?
38) In on-line gaming companies, do you expect the average revenue per user to be larger or smaller than the median revenue per user?
39) How would you increase revenue from advertising clicks if you were working for an ads company (i.e. Google, FB, etc.)?
40) Give an example of a site change that we can't test on a subset of users via a controlled experiment. How would you estimate the impact of that change?