Apple
Weekly Hours:
40
Role Number:
200627262-0836
Summary
Apple is where individual creativities gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. Are you passionate about driving product impact through data, measurement, and rigorous evaluation—at a scale that reaches hundreds of millions of users worldwide?
The AMT Streaming Media Ecosystem team powers the streaming experiences across Apple’s platforms—including iPhone, iPad, Apple TV, Apple Watch, macOS, and third-party devices—by building on foundational technologies such as Core Media and AVFoundation. As a Data Scientist on this team, you will collaborate with Big Data Engineers, Software Engineers, and fellow Data Scientists to leverage data for performance insights, shape new features, and help deliver the best audio and video experiences to Apple users around the world.
Description
As a Data Scientist, you will play a key role in evaluating and advancing Streaming Media technologies. You will partner closely with engineers and multi-functional product leaders to apply analytics rigor, statistical methods, and innovative ML/AI techniques to drive product quality and deliver exceptional user experiences. Your work will shape how streaming frameworks are measured, monitored, and improved at scale.
Minimum Qualifications
Proven track record to frame analytical questions, apply appropriate statistical techniques, and derive actionable insights from complex datasets.
Proficiency in data querying using SQL, Spark, or equivalent technologies.
Strong experience with a scripting language for data processing and analysis (e.g., Python, R, or Scala).
Deep expertise in machine learning, statistical analysis, multivariate methods, A/B testing, and sampling techniques.
Excellent communication and multi-functional collaboration skills, with the ability to convey complex concepts to diverse audiences.
Self-motivated, intellectually curious, and adept at critical thinking, with a demonstrated ability to thrive in ambiguous problem spaces.
Preferred Qualifications
Experience designing and building scalable data systems and pipelines to support complex, large-scale analytics initiatives.
Software development experience in C, C++, Objective-C, or Swift, able to collaborate effectively with engineering teams and contribute to production-grade systems.
Master’s degree in a quantitative or technical field such as Statistics, Computer Science, Engineering, Mathematics, or a related discipline.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .
40
Role Number:
200627262-0836
Summary
Apple is where individual creativities gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. Are you passionate about driving product impact through data, measurement, and rigorous evaluation—at a scale that reaches hundreds of millions of users worldwide?
The AMT Streaming Media Ecosystem team powers the streaming experiences across Apple’s platforms—including iPhone, iPad, Apple TV, Apple Watch, macOS, and third-party devices—by building on foundational technologies such as Core Media and AVFoundation. As a Data Scientist on this team, you will collaborate with Big Data Engineers, Software Engineers, and fellow Data Scientists to leverage data for performance insights, shape new features, and help deliver the best audio and video experiences to Apple users around the world.
Description
As a Data Scientist, you will play a key role in evaluating and advancing Streaming Media technologies. You will partner closely with engineers and multi-functional product leaders to apply analytics rigor, statistical methods, and innovative ML/AI techniques to drive product quality and deliver exceptional user experiences. Your work will shape how streaming frameworks are measured, monitored, and improved at scale.
Minimum Qualifications
Proven track record to frame analytical questions, apply appropriate statistical techniques, and derive actionable insights from complex datasets.
Proficiency in data querying using SQL, Spark, or equivalent technologies.
Strong experience with a scripting language for data processing and analysis (e.g., Python, R, or Scala).
Deep expertise in machine learning, statistical analysis, multivariate methods, A/B testing, and sampling techniques.
Excellent communication and multi-functional collaboration skills, with the ability to convey complex concepts to diverse audiences.
Self-motivated, intellectually curious, and adept at critical thinking, with a demonstrated ability to thrive in ambiguous problem spaces.
Preferred Qualifications
Experience designing and building scalable data systems and pipelines to support complex, large-scale analytics initiatives.
Software development experience in C, C++, Objective-C, or Swift, able to collaborate effectively with engineering teams and contribute to production-grade systems.
Master’s degree in a quantitative or technical field such as Statistics, Computer Science, Engineering, Mathematics, or a related discipline.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .