To be honest,Pinterest, everyone wants to take the offer. Who wouldn’t be jealous of the Pre-IPO-like engineering culture and packages comparable to Meta?
But recently we just helped a student get Pinterest’s Data Science Onsite. Following the whole process, my first feeling was: this Pinterest’s DS interview is really deep and the positioning is really “awkward”.
If you’re staring at job listings on Pinterest, or you’ve already been invited to an interview, this article might be your life saver.
Awkward "sandwich cookie": What the hell are you looking at?
Many students fail when they first come to the exam, not because they are technically incompetent, but because they “fail to understand the situation”.
Pinterest’s data post is now very finely divided, but also very messy. They have people in charge of hardcore modeling MLE(Basically SDE requirements), there is someone responsible for looking at the board Product Analyst (PA).
And what we want to talk about Data Science (DS), just stuck in the middle.
The most terrible thing is that Pinterest's DS is often managed by the Analytics group, which means that your interviewer (or even future manager) is likely to be a PA. This directly leads to a very divisive interview style:
- You thought you were going to tear Transformer apart, but he asked you about the premise of the t-test;
- You thought you were going to talk about deep learning, but he asked you to dictate how to deal with Imbalanced Data.
The four rounds of Onsite our students experienced this time not only did not have so many fancy Deep Learning, but instead were the ultimate combination of statistical foundation and business acumen.
Restore the scene: those four-wheeled Onsite that makes people bald
Don't believe those old interviews on the Internet from a few years ago. Take a look at the real test review that just happened this week.
1. A/B Testing: Don’t be that robot that only looks at P-value
The first scene is the highlight. The interviewer did not ask you to memorize the definition, but directly gave you a business scenario:
"If the results of the Control group and the Experiment group are exactly the same (Flat) without any significant improvement, how should you make a decision?"
This question is very stupid. Many students who are stupid after completing the questions will say: "Then just run away again? Or not go online?" Wrong! What is tested here is Trade-off. You need to talk about Launch Criteria, maintenance costs, and whether this Feature has non-metric strategic value. The interviewer wants a Partner who can make decisions, not a calculator.
2. ML Case Study: No need to write code?
This round is very deceptive. Originally, the students thought they would be coding and building models on the spot, so their hands were on the keyboard, but the interviewer said, "Let's just chat."
The topic is classic LTV / Churn Prediction(How to predict whether newly registered users will become long-term users). There is a huge pit here: Imbalanced Data. After all, only a few new users can be retained. If you only talk about the model architecture but ignore the engineering details such as Downsampling, SMOTE or adjusting the weight of the Loss Function, this round will basically fail. Oralizing the whole process is more of a test of your logical closed-loop ability than writing code. If a sentence is not rounded, the loophole will be caught.
3. PA Coding: Physical work that requires hand speed
This round is 3 SQL and 2 Python. It's not difficult, medium difficulty, but the amount of questions is large. This round is purely about basic skills and proficiency. If you are still looking up the syntax of Pandas, or are struggling to write the SQL Window Function, then you definitely don’t have enough time.
4. Statistics Deep Dive: The real “hell difficulty”
This round is a nightmare for many CS to DS players. The interviewer (a very senior DS) brought the topic directly back to the statistics textbook, but combined it with industrial scenarios:
- Alpha Correction: “What would be the problem if I looked at 100 Metrics in an experiment?”
- If you can’t answer this question with Bonferroni correction or Type I Error expansion, you’re basically saying goodbye.
- Hypothesis testing: What are the assumptions of t-test and z-test? (Don’t underestimate this, 90% of people’s minds will be blank when they arrive at the scene).
- Experiment duration: How long does the A/B Test run? A week? Two weeks? Why? (We’re going to talk about Power Analysis and Seasonality here).
Written at the end: Don’t try and make mistakes with your own Offer
After reading the review above, you should be able to feel: Pinterest's DS interview requires both the engineering thinking of an MLE, the business sensitivity of a PA, and a theoretical foundation like a PhD in statistics.
Many students thought they could pass by just swiping LeetCode, but when they arrived at the scene, they panicked when the interviewer asked about two statistical assumptions, or they were as dry as endorsements in the ML Design session.
that's why you need ProgramHelp
In this season where Headcount is more expensive than gold, every Onsite is a war of attrition.
- For Coding: We have real-time assistance to ensure that you can write code quickly and calmly, and can optimize complexity like Senior.
- For those tricky statistics questions: Our experts (Ex-FAANG Senior DS) will teach you the skills in voice. When the interviewer asks "Alpha Correction", not only can you answer it correctly, but you can also talk about common practices in the industry, which will instantly increase your impression score.
If you also want to securely get a job at Pinterest or a similar top data position, contact us now. Leave those complicated statistical principles and Case Study to us, and you just need to be ready to accept the offer.
