Just recently, Quora had a technical interview with a Data Scientist from the University of California at Berkeley, and it really kind of refreshed my understanding of AB Testing. Although there was only one question in the whole interview, the interviewer asked very detailed and deep questions, from metrics to statistical derivation.
Luckily, this time I had a real-time escort from the Programhelp helper team's remote on-line system! During the interview, they were able to fill in the key points at any time through a direct voice connection without any trace. So when the interviewer continuously asked me about power, MDE, and variance, I didn't panic and finished the whole interview with a clear mind.
Overview of interview questions
The topic is a complete AB Test case study:
The company is preparing to launch a new window prompt on the mobile web that will prompt users to download the App.
There are two buttons in the window:
- "I want to download the app"
- "I already have the app"
- There's also an "x" to close the window.
The interviewer's question was:How will you measure the success of this new feature?
Step 1. Clarify Feature & Goal
At the beginning of the interview, the interviewer asked me to start by saying what I understood about feature.
I asked a few clarifying questions:
- Does this prompt disturb the user's normal browsing?
- Is it only displayed for new users?
- Is our goal to increase App downloads or to increase engagement?
As I finished my thoughts, Programhelp's voice-assisted teacher reminded me softly over the headset, "Emphasize the causal chain of goals and targets."
So I immediately added:
"The ultimate goal is to increase app adoption, so our success metrics should reflect that conversion behavior rather than just clicks. "
The interviewer nodded his head in recognition, and this opening pulled straight up the sense of professionalism.
Step 2. Metrics Design + Strengths and Weaknesses Analysis
Moving on to the metrics section, this was the most pressed part of the session.
I'll start by listing a few indicators:
- CTR (percentage of clicks on the "download" button)
- App install conversion rate
- Average sessions per user
- Time spent per session
The interviewer immediately followed up:
"What are the flaws in these indicators?"
I was just about to answer when the Programhelp helper over there reminded, "Talk about robustness and potential confounder."
So I went along with it and said:
"CTR may be pulled up by short-term curiosity and is not representative of long-term retention; Time per user is susceptible to outlier, look at median or segment-level analysis."
The interviewer was clearly impressed with this segment and continued to ask me, "And how would you improve?"
I went on to make up:
"We can add secondary metrics like 7-day retention or re-visit rate to ensure long-term engagement improvement."
The logic of the whole paragraph is very smooth, which is the advantage of having real-time voice reminders - you won't forget the key logic, and you can also naturally connect to the interviewer's follow-up.
Step 3. Designing AB Tests
Next the interviewer switches topics:
"If you were to compare it to the old version, how would you design the experiment?"
I replied:
- Experimental unit: user-level
- Allocation strategy: 50/50 random assignment
- Experimental cycle: run until desired power reached
- Control variables: device, traffic source
The interviewer immediately went into statistical detail:
"How do you determine MDE?"
"What's the relationship between variance and test duration?"
"If alpha = 0.05, how does it affect your sample size?"
The questions themselves were very technical, and I was answering them while Programhelp's teacher assistants were whispering keywords in the headset, for example:
- "Mention power = 1 - beta."
- "Talk about small MDE -> large sample."
- "Add a tradeoff between business impact and test duration."
I added the sentence on cue: "Smaller MDE requires larger sample size, hence a longer experiment duration - but that also means higher opportunity cost."
The interviewer just nodded and said "Exactly."
The whole session was completely paced by me.
Step 4. Analyzing results and statistical tests
For the last part, the interviewer asks:
"How would you test whether the result is significant?"
I replied:
- Differences between treatment and control were examined by t-test.
- If the metric distribution is not normal, use bootstrap or permutation test.
The interviewer then asked:
"If the result is not significant, what would you do?"
The voice assist side immediately reminded me, "Don't just say extend the experiment, add causal exclusion."
I immediately added:
"First, I'll check whether the experiment was underpowered or the variance was underestimated. Then, I'll look for potential segment effects-maybe certain user groups reacted differently."
The interviewer listens and says "Good, that's the kind of thinking we expect."
A sigh of relief went straight to his heart at that moment.
Interview summary
Quora's DS interview was more of a mind pull.
It's not about how many definitions you've memorized, it's about how well you can tell the full story with data from the product goals.
And therein lies the difficulty of such questions:
- Interviewer follow-up too intensive
- Statistical issues tend to get stuck
- Once the rhythm is messed up, it's hard to get it back.
Programhelp's real-time voice assistants can help you steady your pace, retain logical clarity, and not be led by the interviewer's pace of questioning.
OFFER Acceleration Tips: Interview Stuck? We'll help you get started in real time!
Pain Point Direct: Many students in AB test case interviews like Quora, Airbnb, Meta, etc., not that they can't do the questions:
- When you get nervous, you forget all about logic!
- Once pressed, thoughts were instantly disorganized!
North American CS Specialist Real TimeInterview assistance（Programhelp's professional team uses artificial real-time voice guidance to help you stabilize your rhythm and make up your thoughts, and the effect is far better than AI. hundreds of students have been helped to get DS Offers from Quora, Lyft, Airbnb, Meta and so on, especially in product analytics case round. We have helped hundreds of students get DS Offers from Quora, Lyft, Airbnb, Meta, etc., especially for product analytics case round!
