Sr. Data Scientist, Experimentation - Apple Media Products at Apple (Cupertino, CA)

The Apple Media Products Engineering team is one of the most exciting examples of Apples long-held passion for combining art and technology. These are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books. And they do it on a massive scale, meeting Apples high expectations with high performance to deliver a huge variety of entertainment in over 35 languages to more than 150 countries. These engineers build secure, end-to-end solutions. They develop the custom software used to process all the creative work, the tools that providers use to deliver that media, all the server-side systems, and the APIs for many Apple services. Thanks to Apples unique integration of hardware, software, and services, engineers here partner to get behind a single unified vision. That vision always includes a deep commitment to strengthening Apples privacy policy, one of Apples core values. Although services are a bigger part of Apples business than ever before, these teams remain small, nimble, and cross-functional, offering greater exposure to the array of opportunities here.
Our team provides insights through data that drive decision making for our engineering and product teams. We are looking for a Data Scientist that can derive valuable insights and help us build automated reporting tools from the data we collect on AppStore, Apple Music, Movies & TV, etc.

Our team designs, executes and builds tools for online experiments (A/B tests) and offline experiments (human relevance judgment) that help us improve and fine tune our data-driven features (Search, Recommendations, etc.). Your primary focus will be on applying statistical methods, develop A/B testing procedures, and automating data pipelines to develop new KPIs. Additional expectations of this role include: Data mining using state-of-the-art methods. Investigate new data sources that can extend and improve our insights. Work with software engineering teams to enhance data collection procedures. Processing, cleansing, and verifying the integrity of data used for analysis. Doing ad-hoc analysis and promptly presenting results in a clear manner. Generate reports (that can be automated) to present key insights to partners across engineering and product teams. Designing experiments that will measure and test for key performance indicators. Creativity in formulating statistical questions to address business needs.

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