Day-in-the-Life of a Data Scientist at Apple

Apple is known for being one of the most data-driven companies in the world. But what does that mean for its data scientists? Read on to find out more about the day-to-day responsibilities of a data scientist at Apple.

data scientist apple day in the life

Apple’s data scientists get to do influential work across the company’s software, hardware, and services divisions, helping shape the experiences of billions of people who use Apple products every day.

Sneha Runwal, a former Springboard mentor who started at Apple as a data scientist and has since advanced to the role of machine learning manager, spoke about her experience of breaking into data science and the skills she used in her role. Below are some of the conversation highlights. 

What does a day in the life of a data scientist at Apple look like?

The specific roles and responsibilities of a data scientist at Apple will vary depending on the team they join. For example, a data scientist on a marketing or business analytics team will use advanced statistical, machine learning, and analytical techniques to analyze product performance and identify key insights. A data scientist on the Siri Search team might spend more time on machine learning algorithms and artificial intelligence to help improve Siri’s accuracy. And a data scientist working on security might focus on predictive modeling for fraud prevention.

Apple is known for being a demanding workplace where it is not unusual for employees to work long hours, particularly in the lead-up to one of the company’s high-profile product launches. But many current and former data scientists have described the experience as being worthwhile because they get to work alongside passionate co-workers on impactful products and services, and are well compensated with benefits that rival the likes of Google, Facebook, and Amazon.

Responsibilities and skills of data scientists at Apple

Anyone wanting to break into data science at Apple needs to have foundational data science skills such as knowledge of the complete data science pipeline, proficiency with programming languages such as Python, SQL, and C++, and experience with data analysis, statistics, machine learning algorithms. But there are a few additional skills that Sneha Runwal, an Apple data scientist-turned machine learning manager, says she uses in her role.

  • Data visualizations

“Normally, a lot of our time is spent understanding the nuances of how a particular algorithm is working,” Runwal said. But, given that a large part of a data scientist’s job is to communicate their findings and recommendations to non-technical stakeholders, she believes that data scientists should devote more time toward presenting their findings in a clear, compelling, and comprehensible way. “Data visualization is the part which helps us to identify and communicate the results better,” she said. “So, if there is something that I would like to change or go back in time to focus more on, that would be spending more time doing data visualization and trying to understand how my visualization will convey something different to different audiences.”

  • Filling in the gaps

Even at a data-rich company like Apple, data scientists can run up against sparseness and constraints, which is why Runwal says it’s essential to be able to use your own understanding and logic to fill in those data gaps. “When people...are trying to adjust to the new data science environment, they should keep thinking: What if I didn’t have this data? How would I do this differently? That is the one exercises that I constantly keep on doing.” Runwal said that when she tries to answer questions about what she would do differently, it often helps her come up with new ways to solve data science problems. 

  • Patience and thoroughness

In her capacity as an Apple data scientist, Runwal said it’s important for people in her position to take the time to understand the data they’re working with. The better the data is understood, the easier it will be to also understand the problem and identify a solution. “If you dabble without understanding it completely, you might go in haphazard directions,” she said. “Try to understand your data at the earliest, as much as you can.”

Tips for succeeding as a data scientist at Apple

As someone who started out in software engineering and business, Runwal saw her prior experience as additive to her data science career at Apple. Below are some of her tips for success for those starting their careers in data science—whether fresh out of school, or changing professions.

  • Find connections

“See how your current field relates to data science,” Runwal said. “Once you start building that bridge, it’s easier to transition.” For example, if someone currently works in HR, it’s worth asking how data can be used to make their role more effective and efficient. Or, if someone works in sales or marketing, how might data help their team reach their business goals? Runwal suggests that people ask themselves how they would approach the problems they are trying to solve if they were data scientists. “When you interview [for a data science role], it helps the hiring manager understand why you chose data science,” Runwal said.

  • Tackle a project end-to-end

“For all aspirants: try to do a lot of projects on your own,” Runwal said. “When you don’t have relevant experience, these projects help you to make a strong point that shows your interest. You could either do a project on Kaggle or on your own, or write some data blogs. Those prove that you are definitely interested in data science.”Whenever Runwal is involved in the hiring of new data scientists at Apple, she said she looks for end-to-end implementation, even if it means that a person has only listed one project on their résumé.“What I’m looking for is, has he done an end-to-end solution?” said Runwal, who added that a person who has completed a project from start to finish can establish domain expertise and show that they can do everything from exploratory data analysis, to production coding, to sharing their findings. “[It] shows me that the person is independent enough to gather the data on their own and...make it an end-to-end complete solution.”

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