Would you like a chance to be part of a diverse team driving product impact using meticulous measurement and evaluation, for products and services used by hundreds of millions of people globally? The vision for the AIML Data organization is to create innovative technologies, methodologies, and research to enable amazing user experiences and advance the frontier of machine learning. Within this organization the mission of the Search Analytics team is to inform product and methodology evolution through measurement, evaluation, and analysis of the user experience. You will partner with various engineering teams to improve product quality and guide feature development with data, to deliver amazing search experiences across iPhone, iPad, HomePod, Mac, Watch, tv, across dozens of languages.

Research and develop evaluation methods to improve the quality of Apple user facing products with evaluation/experimentation engineering teams to get your methodological developments translated into technologies that product engineering will use every day.

Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed.

Conduct analysis that includes data collection and quality control, requirements specification, processing and presentations. Use data from multiple types of sources including human annotation data.

Build and prototype analysis pipelines and visualizations iteratively to provide insights at scale. Develop comprehensive knowledge of product data structures and metrics, advocating for changes where needed for product development. Partner closely with product engineering teams on core machine learning algorithms and user experience evaluations.

You should be passionate about building outstanding products using rigorous and innovative ways with the data at hand. You are determined to making and sharing impactful work through methodological development and articulate and persuasive communication.

Experience in data science, machine learning, and analytics, including statistical data analysis and A/B testing.Using Foundation Models for data science: prompt engineering and fine-tune foundational models for specific tasks.Strong communication skills and the ability to naturally explain difficult technical topics to everyone from data scientists to engineers to business partners.Strong programming skills, including data-querying skills (SQL) and experience with Python scripting language for data processing and development. Be able to translate ideas/methods into code and dashboards without hesitation.M.S in Statistics, Machine Learning, Computer Science, Operations Research or related areas.