Recent Apple The position of Data Scientist was released, and the popularity started to rise immediately. I just finished a complete interview process here, and the overall experience is quite representative: the questions are not biased and not difficult, but the coverage is very comprehensive. It not only tests the basics of statistics, but also coding, and also focuses on your understanding of products and business. So today I will give a complete review of this interview, from statistics to SQL, to modeling and business sense, basically going through the common test points, and giving it a reference for students preparing for Apple DS.
Round 1: Statistics + Projects
The overall course is relatively easy, with no coding. It mainly tests the basics of statistics and understanding of business.
- Dig deep into your resume: Ask about method selection, data processing, and some follow-ups around the project, focusing on whether you really understand what you have done.
- Statistics questions:
- The confidence interval is shortened to 1/10 → essentially the sample size needs to be expanded to 100 times
- Comparison of student scores before and after → Use paired t-test, H0 is the mean difference = 0, because they are the same group of students, it is a paired test
- Product issue (search scenario): How to evaluate new features, mainly looking at CTR, conversion rate, search efficiency, whether to reduce repeated searches and other indicators
The overall idea is: don’t make mistakes in statistics + if you can explain the business clearly, there won’t be much of a problem.
Round 2: Technical
The second round is a purely technical interview, which lasts about 60 minutes and is divided into three parts: SQL, Python Coding, and Business Sense + Statistical Modeling. It is conducted in a shared code editor. The interviewer requires you to explain your ideas while writing.
SQL
The first question is the overlapping problem of iPhone and iPad users. The essence is to split the set: the proportion of people who only use iPhone, only use iPad, and those who use both, account for all users. The key point is to remove duplicates (distinct) and use overall unique users in the denominator, which is a relatively standard set + aggregation problem.
The second question is to find the highest-paid employee in each department. It is a typical window function question. Use ROW_NUMBER or RANK to sort by department group, and just take the first place. It is basically a high-frequency interview template question.
Python coding
The first question is matrix multiplication: given two matrices, first determine whether they can be multiplied (whether the number of columns is equal to the number of rows), if so, output the product matrix, otherwise return an empty list []. After writing, the interviewer asked about the time complexity (O(m×n×p)), and briefly discussed how to optimize it if it is a sparse matrix (for example, using a sparse matrix storage format and only processing non-zero elements).
The second question is string processing: given a sentence, there may be multiple spaces between words. It is required to completely reverse the order of words while ensuring that there is only one space between words. For this question, you can use strip().split() to process excess spaces and then reverse and join, which is very simple and efficient.
Business + Modeling
The scene is a free trial of Apple News. The interviewer first asked how to evaluate the effectiveness of this campaign. The core here are conversion rate (trial → paid), retention, user activity, and long-term revenue (LTV). You cannot just look at short-term indicators. Then let you design a model to predict whether trial users will eventually pay. This is essentially a two-classification problem. A common idea is to use Logistic Regression as a baseline, and then use a tree-based model (such as GBDT) to improve the effect. The features are built around user behavior (reading frequency, duration), content preferences, and behavioral changes during the trial.
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