Apple is a technology conglomerate that designs, manufactures, and sells consumer electronic goods, software/apps, and a variety of online services. Given the massive amounts of data its platforms such as Siri, iCloud and other online services constantly generate and handle, Apple needs data scientists who can help its platforms make sense of these huge data.

The role of a data scientist at Apple is different from those at other companies. It differs because Apple needs what is called full-stack data scientists. Apple expects its data scientists to be dynamic enough to work in different roles across its various teams such as the machine learning/AI-centric Siri team, analytics-heavy iTunes team, marketing, and sales teams, etc.

Practical experience with and theoretical understanding of various machine learning algorithms

Working knowledge of relational databases, including SQL, and large-scale distributed systems such as Hadoop and Spark

Mathematics, computing, and statistical skills

Coding skills in a general programming language such as Python, Scala, or Java

Ability to extract meaningful business insights from data and a knack for identifying patterns.

Excellent communication and presentation skills

Median salary: 250,000 USD, with base component being 175,000 USD, stock component being 100,000 USD, and bonus being 25,000 USD.

What is the Salary offered to an Apple DS?

Technical Interviews aren't easy! Prep with an Apple DS and ace your interview!

Once your application is shortlisted, you will get an initial call from the company's hiring manager/HR manager. This telephonic screening round usually lasts for anywhere between 30 minutes to an hour. The HR asks some basic questions related to your CV, your prior work experience, projects that you handled etc. This screener may also include the common HR questions such as why you wish to join Apple, where you see yourself ten years down the line and so on.

Given a table of user log with platform info, count the daily active users on each platform(iPad, iPhone, Mac)

Give the difference between Random Forest and xgboost.

What is the Propensity model and how beta estimates are calculated by MLE?

What is the Time series Model and give the meaning of ACF and PACF?

What is the Bias-Variance tradeoff and give ways to check the overfitting of a model?

Questions regarding quality assurance of a design or product

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 in meaningful segments?

Given a list of strings, write a function in Python to return all the strings that are anagrams.

The technical screening round tests the soundness of your knowledge in data science algorithms, coding, statistical and mathematical skills, communication skills and so on. Typically the questions will be on relational databases such as SQL, large distributed systems like Hadoop, coding languages such as Python, Scala, machine learning theory and also some reasoning questions on data science. You will be judged on how succinctly and swiftly you are able to explain your solutions to the hiring manager using fundamental concepts.

Sometimes the technical screener may also include a take-home challenge. You may be given 3 to 7 days to complete the challenge. The take-home challenge, in most cases, is a machine learning-based problem asking you to build a predictive model for certain datasets.

Practice more technical questions with an Apple DS

Ace your Apple DS interview by practicising questions with an Apple DS!

The candidates clearing the technical screener will be called for an onsite interview at one of Apple's campuses. The onsite interview consists of anywhere between 5-7 interviews. The interview panel will consist of 2-3 panelists who would be Apple's Data Scientists, product managers, and HR managers. Apple's onsite interviews revolve around the role the interviewee is applying for. So, it's wise to prepare for and understand the roles and responsibilities of the position before you appear for the interview. 

Onsite interview with Apple's Data Scientists:

This interview would mostly focus on your ability to apply data science algorithms to real-life situations. You may also be asked reasoning questions based on data science.

In the past, questions based on machine learning theory such as classification, regression, clustering, and anomaly detection have also been asked.

Onsite interview with Apple's Product Managers:

In the product manager interview of the onsite loop, Apple has got a vast repertoire of questions to ask from. Questions may be asked regarding the improvement of Apple's existing platforms such as iCloud, iTunes, Siri, etc. You may also practice questions on relational databases such as SQL, large distributed platforms such as Hadoop and Spark.

Onsite interview with Apple's HR managers:

The questions in this section are usually open-ended. The HR managers may ask you behavioral and motivational questions such as "Why apple?" or "What drives you?". A candidate was asked the question " If you had one superpower what would it be?"

So, the trick here is to be confident in your answers and speaking what you feel.

The Apple Data Scientist interview might be a tough nut to crack but it becomes easier if you are thoroughly prepared. To summarise, you must be thorough with your CV, projects done, have the necessary knowledge about Machine learning algorithms and their applications, coding abilities especially in Python, knowledge about SQL database system and skills in statistics and mathematics.

The Apple Data Scientist Interview Guide is a comprehensive resource designed to help individuals prepare for data scientist interviews specifically at Apple.

What is the Apple Data Scientist Interview Guide?

The guide covers a wide range of topics, including technical concepts, problem-solving approaches, behavioral questions, and tips for success in the Apple data scientist interview.

The guide provides valuable insights into the interview process at Apple, offering tips and strategies to enhance your preparation. It includes sample questions and answers to help you practice and familiarize yourself with the types of questions you may encounter.

Yes, the guide covers various technical areas commonly tested in Apple data scientist interviews, such as statistics, machine learning, data analysis, and programming languages like Python or R.

Are there specific technical areas covered in the guide?

Yes, the guide includes guidance on how to approach behavioral interview questions, highlighting the key competencies and qualities Apple may be looking for in a data scientist candidate.

Does the guide offer behavioral interview advice?

Yes, the guide is designed to benefit candidates at different experience levels. It provides foundational knowledge as well as advanced concepts, making it applicable to both entry-level and experienced data scientists.

Is the guide suitable for both entry-level and experienced candidates?

The Apple interview process is similar to that of other major tech companies such as Facebook, Google, and Amazon. Candidates are required to submit their applications online. Those who make it to the interview round receive a call from the recruiter, usually an HR manager for the company. After that, candidates face a technical phone screen. Those who make it beyond this screening stage are called for an elaborate onsite interview. The location for the onsite interview may vary according to roles. It may either be in Cupertino, Austin or some other Apple campus.

Prepare for your Apple Data Scientist interview with our comprehensive interview guide. Get insights, tips, and sample questions to ace your interview at Apple.

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Detailed, specific guidance on the Apple Data Scientist interview process - with a breakdown of different stages and interview questions asked at each stage

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Apple Data Scientist Interview questions

Could you explain how the K-means and Expectation-Maximization (EM) algorithm differ in their approaches?

Can you give an example of a job that was the most rewarding for you, and what aspects of it made it so?

You're responsible for the execution of new AML regulations at Amazon Web Services, what are some of the things you'd think about?

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