Machine Learning (ML) Data Scientist - ISE Analytics and User Studies, Input Experience

Apple's Intelligent System Experience (ISE) group builds intelligent software products and experiences on platforms that are used by millions of users around the world every single day. The ISE Data Science team works across products and platforms to translate vast amounts of metrics about software feature usage on iPhone, iPad, and Mac into actionable insights for our leadership in Engineering, Design, and Product Marketing teams. As Apple Intelligence features such as Writing Tools, Smart Reply, and Genmoji evolve, data has become increasingly central to how we understand the user experience, inform product strategy, and evaluate new machine-learning systems in a timely fashion.

We are looking for an experienced Machine Learning (ML) Data Scientist to help us uncover insights about how Apple customers experience our technologies. This includes but is not limited to: \n\n*Understanding feature usage, such as engagement with Genmoji and Image Playground \n*Evaluating the accuracy of algorithms like autocorrection for non-English languages, and ML models \n*Mining user feedback data to identify opportunities for model improvements

Master's or PhD degree in Data Science, Statistics, Engineering, or other related field plus 3+ years of industry experience building data-driven solutions to solve business problems.\n2+ years of experience with scripting languages (e.g. Python) for data processing and development, and 2+ years of data-querying experience (SQL and/or Splunk, etc.).\n2+ years of experience working with Tableau or similar data visualization technologies as well as the ability to design informative dashboards and reports.\nA solid foundation in statistical analysis, capable of designing A/B experiments and conducting analyses on your own.

Excellent communication and presentations skills, including the ability to clearly explain difficult technical topics to business partners and executives.\nAbility to dive into the details on product features and underlying architecture in order to define metrics and success criteria.\nAbility to work effectively cross-functionally with Engineering, User Studies, QA, Design, Marketing, and others across multiple time zones and across multiple projects simultaneously, delivering meaningful insights in a timely manner to drive project success.\nExperience working with and processing non-English or multilingual data (e.g. Cantonese, Thai, Arabic). Passionate about understanding global cultures and languages.\nExperience with generative models and leveraging them to create applications.\nExperience working with big data, and applying machine learning to solve business problems, especially around launching of new products and services.\nExperience with Spark or PySpark applications.\nExperience with natural language processing.