App Store Data Scientist
Apple · Cupertino, CA · Software and Services
At Apple, we have phenomenal ideas that have a way of becoming great products, services, and customer experiences! We are seeking a Revenue and Subscriptions Data Scientist to join a team passionate about Data Science & Analytics for Apple Services to support transaction and subscription businesses across App Store, Apple Music, Apple TV+, Apple Arcade, Apple One, and other services!
You will play a key role understanding and optimizing the businesses under Apple Services as a part of the Revenue and Subscriptions Data Science team. As a member of this team, you will help us to grow the financial, transaction, and subscription health of our businesses by sizing, measuring, and recommending impactful initiatives. Responsibilities will include understanding the impact across in-app purchases, subscription renewals, payment authorizations, transaction efficiencies across multiple lines of businesses.
<h3>Minimum Qualifications</h3>5+ years of professional experience in data science, machine learning, or digital product analytics
Mastery in SQL-based languages, and proficiency large-scale data languages such as PySpark
Proven record measuring user experience behavior, customer engagement, and business impact using sophisticated and appropriate analytic tools
Strong proponent of experimental test and design, and practical experience with interpreting observational results
Excellent communication and presentation skills with meticulous attention to detail with the ability to communicate effectively between business and analytics teams
Strong verbal and written communication and presentation skills across both technical and non-technical audiences
Bachelors degree in Computer Science, Statistics, Mathematics, Engineering, Economics or related field
<h3>Preferred Qualifications</h3>Experience in a digital subscription business or for an e-commerce platform
Masters degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Economics or related field