Apple Data Scientist Interview Questions + Guide in 2024

Apple Inc. is one of the biggest technology companies globally that designs, develops, and sells consumer electronics, computer software, and online services. Apple is constantly in need of creative, passionate, and dedicated data scientists that can sit on any number of their teams. From its researched-based artificial intelligence development team at Siri to cloud-based architecture development team at iCloud, Apple has slowly but steadily been building data science teams to handle the avalanche of data accumulated on a daily basis.

The role of a data scientist at Apple varies a lot and is dependent on the teams you are assigned to. The actual title of data scientist at Apple functions as the closest thing to a full-stack data scientist. This means the job will require everything from analytics to machine learning software design to plain engineering.

Given Apple is a huge multi-conglomerate, the data science skillset used will vary by teams as there are many analytics teams across various divisions like marketing, finance, sales, etc as well as more machine learning and deep learning based teams on products and services like Siri, cloud services, and even hardware.

Required Skills

Apple, for the most part, prefers to hire applicants with at least a few years of experience under their belt as a data scientist. The requirements are as follows:

• 3+ years’ experience (5+ years for a senior position) applying data science to real business problems
• Practical understanding of machine learning techniques such as regression, time series analysis, clustering, decision tree techniques, and experience with algorithms for classification.
• Working knowledge of relational databases, including SQL, and large-scale distributed framework such as Spark and Hadoop.
• Proficiency in numerical and scripting programming languages like SQL, Python, Java, C++, PHP, or Perl
• Excellent presentation skills, distilling complex analysis and concepts into concise business-focused takeaways

Technically speaking, there are no specific types of data scientists that apple hires. Apple hires based on different team’s needs and skills needed. There are data scientists that work on largely analysis work, across many divisions as well as machine learning heavy roles. Depending on the teams, the functions of a data scientist at Apple may include:

• Collaborate with business teams to discover insights, opportunities, understand requirements, and translate those requirements into technical solutions.
• Collaborate with data engineers and platform architects to implement robust production real-time and batch decisioning solutions.
• Use machine learning to automate processes (like scoring).
• Design, develop and manage big data-driven predictive models to improve user engagement using the latest technologies in machine learning, user pattern recognition, and data modeling.
• Working with large scale data; manipulating and extracting data using Sparks SQL.

The interview process for data scientists at Apple is pretty standardized. The interview process starts with a preliminary phone screening by HR, then a hiring manager interview to assess further interest and role fit, and a brief technical phone screen. Finally, there is maybe a take-home assignment depending on seniority and position type before an on-site interview.

Technical Screen and Take-Home Challenge

The next step is the technical hiring manager phone screen and possibly a take-home challenge. The technical hiring manager screen is done in a shared coding environment.

The data science interview questions cover general python exercises and data science reasoning questions. It is important to talk through your thought process in the technical screen and communicate your assumptions clearly. Here your ability to make use of basic data structures and algorithms concepts are tested. The key skill required here is the ability to provide a comprehensive solution and swiftly analyze the runtime complexity of the solution.

The Apple data science take-home challenge is given with a set time limit of three days to complete. Usually, the challenge will be a machine learning problem to build a model and make a prediction off of a dataset.

Example Apple Data Scientist technical screen questions:

• Given a list of integers, find the index at which the sum of the left half of the array is equal to the right half.
• How do you take millions of users with hundreds of transactions each, amongst thousands of products and group the users together in meaningful segments?
• Given a list of strings, write a function in Python to return all the strings that are anagrams.

The Onsite Interview

The last step is the on-site interview. The interview panel consists of 5 to 6 interviews usually on the team of the position that is being interviewed for. Each interview consists of one to two interviewers with a lunch arranged on the Apple campus with the hiring manager. Note that while it may be an informal setting, the lunch interview is very much the cultural fit part of the interview.

Onsite Notes

• The on-site interviews are separated in the feedback from one interview to another. If you didn’t perform well in one interview, the feedback won’t accompany you into the next interview.
• Remember the title Data Scientist at Apple cuts across a wide range of teams related to data science. It’s helpful to ask the recruiter what will be on the onsite interview given the wide-range of requirements for a full stack data science position. If the data science position is more analytics focused, it will help to practice a lot of SQL and product case-based questions. If the position requires model-building and machine learning, review concepts on machine learning system design and implementation.
• Generally when faced with white-boarding coding interviews, Apple interviewers seem to favor questions on linked list, arrays/string and system design.
• Data scientists at Apple can be very highly paid depending on what level you fall into. An example salary for an individual contributor at level 4 could be $150–$180k base salary with a ten percent bonus and $200–$300k in stock over four years. The refreshers for stock can also be very high every year.

• Describe the difference between L1 and L2 regularization, specifically in regards to the difference in their impact on the model training process.
• What is the meaning and calculation of ACF and PACF?
• How would you design a client-server model where the client must send location data every minute?
• Write a function to detect if a binary tree is a mirror image on both the left and right sub-trees.
• Let’s say you have a time series dataset grouped monthly for the past five years. How would you find out if the difference between this month and the previous month was significant or not?
• How does XGBoost handle the bias-variance trade-off?
• Suppose you have 100,000 files spread across multiple servers and you wanted to process all of them? How would you do that in Hadoop?

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