Data Scientist, Aiml Data Scientist

Apple Apple · Big Tech · Cupertino, CA +1 · Software and Services

This role focuses on data operations and capacity planning within an AI/ML context, specifically analyzing annotator performance, task complexity, and data characteristics to optimize data annotation workflows and project scoping. The goal is to ensure high-quality annotated data for unreleased products and AI technology by improving efficiency and quality through data-driven insights and experimentation.

What you'd actually do

  1. Analyze trends across projects to identify patterns in annotator performance, task complexity, and data characteristics that inform more effective project scoping design and guideline development.
  2. Evaluate which tasks and data types benefit most from automation and work with customers to optimize against.
  3. Translate behavioral insights and empirical trends into optimized project structures, ensuring our annotation workflows are designed for both efficiency and quality before projects launch.
  4. Partner with cross-functional teams to design, run, and analyze A/B experiments, establishing best practices for project structure.
  5. Contribute to capacity forecasting and optimization by converting quantitative decision-making into forecast drivers and scenarios.

Skills

Required

  • SQL-based languages
  • Python or R
  • scalable schema designs
  • relational database and big data technologies
  • ETL
  • code management
  • query performance optimization
  • developing meaningful and concise analytic objectives from general business goals
  • interpretable machine learning models
  • sophisticated analytic solutions

Nice to have

  • Masters degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Economics or related field
  • deployment of Large Language Models / Generative AI in service of efficiency in operations
  • Excellent communication and presentation skills
  • meticulous attention to detail
  • ability to collaborate effectively between business and analytic teams at multiple levels of the organization
  • Passion for AIML and Operations
  • consistent track record of operational results

What the JD emphasized

  • 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
  • Strong hands-on experience interpretable with machine learning models and sophisticated analytic solutions using scripting tools such as Python or R

Other signals

  • delivering high-quality annotated data
  • optimize task design
  • capacity planning & optimization
  • modeling & experimentation
  • translate behavioral insights and empirical findings into optimized project structures and workflows