Apple's data scientists can collaborate on important projects across the company's software, technology, and services divisions, influencing the lives of millions of individuals using Apple's products every day.
Sneha Runwal, a recent Springboard mentor who began her career at Apple as a data scientist and subsequently moved to the position of machine learning leader, discussed her experience breaking into data science or the skills she uses in her profession.
Apple is well-known to be among the world's most data-driven companies. But what does this mean for the company's data scientists? Before we move on to the roles and responsibilities of Apple data scientists, feel free to check out the top data science course in Mumbai.
What is a typical everyday life of an Apple data scientist like?
A data scientist's particular roles and responsibilities at Apple will vary based on the team they join. For example, a data scientist on a marketing or business analytics team will analyze product performance to find significant insights using complex analytical, machine learning, and other analytical tools. To boost Siri's accuracy, a data scientist in the Siri Search team may devote more time to machine learning techniques and artificial intelligence. A data scientist specializing in security may concentrate on predictive modeling for fraud prevention.
Apple is notorious for having a challenging workplace where employees frequently work long hours, especially in the run-up to one of the company's high-profile product launches. Yet, many current and previous data scientists have praised the experience as rewarding since they work on important products and services with dedicated coworkers and get well compensated with perks that rival Google, Facebook, and Amazon.
Roles and Responsibilities of Apple data scientists'
Everyone who wants to work in data science at Apple must have fundamental data science skills, including understanding the entire data science pipeline, fluency with computing programming languages like Python, SQL, and C++, and familiarity with data analysis, statistics, and machine learning methods. Yet, Sneha Runwal, former Apple data scientist to machine learning manager, claims to apply a few more talents in her work.
Data Visualization
"Normally, we devote tremendous energy to learning how a particular algorithm works," Runwal observed. Yet, she believes that because communicating findings and recommendations to stakeholders is such an essential part of the job of a data scientist, data scientists should devote more time and attention to articulating their thoughts in a plain, convincing, or accessible manner. "Data visualization allows us to identify and analyze the consequences," she stated. "Hence, if I could go back in time and change one thing, it would be to devote more time to data visualization and investigate the ways in which it may communicate different concepts to a range of audiences.."
Bridging the gaps
Data scientists may face shortages and constraints also at Apple, a leading content company, and that is why Runwal feels it is essential to have the ability to cover a certain amount of poor data with your abilities and logic. "When people try to adjust to the additional information of scientific culture, they will constantly ask themselves, "Well, what might I have done differently if I hadn't ever received this data?" That's the only exercise I perform regularly. Runwal stated that addressing questions about what she may do otherwise frequently helps her develop new solutions for data science problems.
Patience and thoroughness are required.
As an Apple scientist, Runwal believes people like your position must find hours understanding the data she is working with. The better basic facts are understood, the quicker it will be to identify the problem and find a solution. "Should you dabble without knowing what you are doing, you can end yourself going in unexpected areas," she said. "As rapidly as possible, try to absorb all your information."
Strategies for Succeeding as an Apple Data Scientist
Runwal, who began her career in computer programming and business, considered her former skills valuable to the data science work at Apple. The following are a few of her success suggestions for individuals just beginning out in data science, whether they are newly graduated from college or changing fields.
Find Connections
"Think about how your present field ties to data science," Runwal suggested. "Once you begin constructing that bridge, it will be simpler to adjust." In contrast, if one remains in the HR department, it is useful to inquire about how statistics might be used to increase the productivity and efficacy of their work. Or, whether you work in sales or marketing, may data help your team accomplish their business goals? Runwal urges people to consider how they could approach the challenges they hope to address as data scientists. "Whenever you interview for a data science position, it benefits the agreed to give why you chose data science," Runwal noted.
Complete a job from start to finish.
"All candidates should try to complete that many assignments on their own." Runwal proposed. "When you lack relevant experience, many projects enable you to build a solid argument demonstrating your commitment." You might establish a business on Github or your own or publish some data blogs. They demonstrate your keen fascination with information technology." When hiring additional data scientists for Apple, Runwal explains she seeks end-to-end solutions, even though this indicates a person has only one job on their résumé. If you want to work as a data scientist at MAANG companies, register in the top data science course in Pune available online.
