Data Solutions Engineer, Retail Customer Insights

Apple Apple · Big Tech · Cupertino, CA +1 · Corporate Functions

This role focuses on designing and building data infrastructure, pipelines, and ML workflows to support customer insights research in Apple Retail. It involves modernizing research operations, deploying ML/NLP models for feedback analysis, and integrating LLM capabilities. The primary output is data infrastructure and ML-powered analysis tools, with a secondary focus on agentic capabilities for analysis.

What you'd actually do

  1. Architect data solutions that support the full research lifecycle, including survey infrastructure, data pipelines, automated reporting systems, and AI-enabled analysis workflows
  2. Lead the modernization of research operations by identifying opportunities to automate manual processes, improve data quality, and accelerate time-to-insight across the team
  3. Partner closely with researchers, data analysts, and technical teams to translate business and research needs into robust, scalable technical solutions
  4. Deploy machine learning and NLP models to extract themes, patterns, and actionable insights from large volumes of structured and unstructured customer feedback
  5. Maintain the team's tooling ecosystem, ensuring systems are reliable, well-documented, and positioned to scale with growing data volumes and business complexity

Skills

Required

  • Python or R
  • SQL
  • data querying at scale
  • building or applying machine learning and AI techniques
  • NLP
  • LLMs
  • classification
  • clustering

Nice to have

  • modernizing or operationalizing research or analytics workflows
  • automation pipelines
  • integrating AI-powered tools
  • data visualization and reporting platforms
  • survey platforms and systems
  • survey methodology
  • research concepts
  • customer experience measurement
  • documentation skills
  • system diagrams
  • data flow documentation
  • technical specifications
  • navigating ambiguity
  • managing competing priorities
  • adapt quickly to changing business needs
  • tailor communications

What the JD emphasized

  • build and ship production-quality data solutions
  • machine learning and AI techniques to real-world data problems
  • applying large language models (LLMs) and generative AI
  • integrating these technologies into data products or analytical workflows

Other signals

  • design data infrastructure
  • pipelines
  • machine learning workflows
  • enhance survey systems
  • integrating new LLM capabilities
  • deploy machine learning and NLP models