AI/ML - Data Science Leader, AI/ML Annotations

Seattle, Washington, United States
Machine Learning and AI


Weekly Hours: 40
Role Number:200307932
Our organization is responsible for delivering high-quality, human-annotated, machine learning data at scale to product teams across Apple. This global data annotation and collection initiative enables Apple to create amazing new experiences and advance flagship experiences, like Siri. Our team supports the entire Annotations Operations team - providing insights into the work we do. We are passionate about what we do and creative about how we collaboratively solve problems for the organization. We are currently seeking an experienced and influential Data Science Leader to grow the Machine Learning Annotations Analytics team and function. This position will lead a team of data engineers and scientists to establish top-line health metrics, identify key growth drivers, and recommend operational and business optimizations through scalable, responsive, and interpretable research and analyses. As a key member of our diverse and dynamic organization, you'll have the rare and rewarding opportunity to work with datasets of unique magnitude, richness, and dedication to customer privacy that will frequently require innovative approaches. You'll work collaboratively with partners across the organization daily to deliver material customer and business value.

Key Qualifications

  • 4+ years of experience in leading data science, analytics, or data operations teams - preferably in the Machine Learning data annotations and collections areas
  • 4+ years of experience in data science with proven skills in developing meaningful and concise analytic objectives from general business goals
  • Tested capabilities and comfort in scalable schema designs, relational database and big data technologies, ETL, code management, and query performance optimization
  • Mastery in SQL-based languages, and proficiency in at least one large-scale data languages
  • Strong hands-on experience interpretable with machine learning models and sophisticated analytic solutions using scripting tools such as Python or R
  • Excellent communication and presentation skills with meticulous attention to detail with the ability to collaborate effectively between business and analytic teams at multiple levels of the organization


• Establish a center of excellence for the Data Annotations Data Science team by uncovering business-actionable insights in collaboration across our customer groups as well as the Annotations team. • Lead all aspects of large complex projects from conception to completion, develop roadmaps and requirements, identify risks and develop contingency plans, evaluate impact, and regularly communicate status to executives. • Establish, enhance, and socialize top line and operational metrics that accurately represent the business's state of health. • Lead proactive analyses that identify key drivers of operational-health metrics, and make recommendations that optimize overall business performance. • Develop reporting measures to expand the portfolio of self-service dashboards and reports to inform, enable, and empower relevant collaborators • Draft schema and instrumentation requirements to enrich operational datasets for new projects, etc. • Identify key factors that lead to attrition, declining quality, etc. including historical behavior, project, product, device, and productivity metrics. • Deepen and broaden relationships with peer groups and customers. • Build a comprehensive view of analyst behaviors across the various platforms, and identify commonalities that drive a more positive analyst experience and engagement. • Develop, recruit, and train a diverse team of impactful data engineers and scientists passionate about uncovering insights from large-scale data across all aspects of the operation.

Education & Experience

Minimum of a bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, or related field. A Master's degree in a related field is a plus.

Additional Requirements