Apple is considered one of the top workplaces for data scientists, offering both competitive salaries and benefits, and opportunities to shape products and services used by billions of people worldwide. Read on to learn more about what it’s like to be a data scientist at Apple.
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If there’s one company where a data scientist can work on products and services that are synonymous with innovation and reach billions of people worldwide, it’s Apple.
With one of the world’s most revered hardware divisions and software that spans digital marketplaces such as Apple Music and Apple Arcade; entertainment platforms such as Apple TV and Podcasts; operating systems such as iOS and OS X; payment services like Apple Pay; and cloud computing services like iCloud—there are no shortage of opportunities for a data science specialist to flex their data gathering expertise.
Read one to find out how to get hired as a data scientist at Apple—and don’t forget to check out the guides below!
Apple is known for its culture of secrecy—beyond keeping its hardware and software developments under wraps from the public, even employees are often in the dark when it comes to the company’s latest projects. But this doesn’t detract from the experience of working at Apple, according to current employees and former interns, who spoke of how rewarding it can be to work on products that end up being used by millions of people.
“It was super cool to get to be intimately involved in particular products before they came out, and to see the gritty details of past products, even some that never made it to market,” said former intern Nate Sharpe.
In addition to generous compensation packages that rival those of Facebook, Amazon, Google, and Microsoft, Apple also boasts a Norma Foster-designed campus at its corporate headquarters in Cupertino and a slew of employee perks. Some of the benefits include:
The base salary for an Apple data scientist depends on years of experience, education, and location, and the total compensation can vary greatly depending on whether someone qualifies for an annual bonus or stock grants.
At the most entry-level of the range, Apple’s data science interns make around $7,000 a month, in addition to complimentary housing, a relocation stipend, and health benefits.
Data scientists who hold an undergraduate degree in computer science, statistics, mathematics, econometrics, economics, or a related field, and have at least a few years of experience under their belt can earn around $144,960, according to Glassdoor, in addition to bonuses and stock grants.
Senior data scientists who also hold master’s degrees or a Ph.D. in a related field, such as machine learning, can make around $179,700, in addition to bonuses and stock grants.
Data scientists who have interviewed with Apple report that the company seeks “full-stack” professionals, which means they want a data scientist who can do everything from large-scale data mining to insight synthesis, working with machine learning algorithms, system design, and software engineering. In order to determine whether qualified applicants check all the boxes, a recruiter will ask both technical and behavioral questions.
On the technical front, applicants can expect questions during both the phone screen and in-person interviews about A/B testing, developing analytical solutions and the methodologies behind them, full-stack data analysis, and how tools such as Hadoop, Teradata, and Spark can be deployed. Applicants should also expect to code in SQL or Python.
On the behavioral front, applicants can expect HR questions about why they want to work at Apple and how they perform under pressure. A recruiter will likely go over a candidate’s CV and ask about specific projects, internships, and past work experience to assess whether an applicant is a self-starter who excels at independent decision-making, collaboration, and has the communication and presentation skills to help Apple’s teams use data to identify and use key insights.
Successful candidates typically hold basic qualifications such as a bachelor’s degree in computer science, mathematics, statistics, or a related field, as well as a strong portfolio that shows experience working with the full data pipeline, effective communication of findings through data visualization tools, and creative problem-solving.
Apple offers two kinds of internships: a conventional summer program, which runs for at least twelve weeks, and a full-time program that runs for at least six months during a regular semester. Former interns and current employees report that interns who do a good job often receive offers to join the company full-time once they graduate from their degree programs.
Skill requirements vary depending on the type of data analyst role and the team an intern joins. For example, most interns are expected to be enrolled in an undergraduate program and have some experience with data analysis and programming languages such as SQL or Python, but more advanced internships in areas such as machine learning and artificial intelligence require interns to be pursuing a master’s degree or Ph.D. in natural language processing, deep learning, statistical machine learning, or a related field.
The internship process begins with online application submissions, followed by a series of interviews. In addition to assessing whether an applicant has the technical chops to learn fast and contribute to Apple’s teams, the hiring manager also wants to see whether a candidate is a cultural fit.
“These interviewers aren’t just looking for someone who is very good in one specific field,” said former Apple intern Caitlin Connerney “They’re also looking for a coworker, a teammate, and a friend. For Apple, you need to be good in every applicable area."
An Apple data scientist’s day-to-day is largely determined by the product or business teams they’re on. While all of Apple’s data scientists are skilled in SQL, Python, data processing, experiment design, predictive analytics, and operations research, and can collaborate with other teams, meet tight deadlines, and clearly communicate their findings to stakeholders, they each apply their skills to different areas of the business.
For example, a data scientist on the Siri Search team might focus on machine learning algorithms and artificial intelligence to improve Siri’s accuracy. A data scientist in Apple’s acoustics hardware division might craft experiments to quantify performance. And a data scientist working on improving customer experiences of Apple’s marketplaces might perform deep quantitative research of the App Store, the findings of which could influence the company’s business strategy.
One of the key things that Sneha Runwal, former Springboard mentor and an Apple data scientist turned manager of machine learning, said she looks for in the data scientist hiring process is whether a candidate has completed a project end-to-end, because this shows her that they are familiar with the complete data pipeline, have had hands-on experience with a project, and can work independently.
Springboard’s Data Science Career Track prioritizes these very elements of the data science experience. In addition to teaching foundational data science skills such as data wrangling, inferential statistics, assessing data quality, understanding data pipelines, and workflows, and implementing machine learning algorithms, emphasis is placed on building real-world projects.
Springboard students will work on multiple small projects, plus two capstone projects, that will give them hands-on experience with coding in Python, coming up with questions they can ask of a data set, designing a project roadmap, querying data, rapid iteration, and model evaluation.
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