Data Science Manager – Strategic Data Solutions

Apple Apple · Big Tech · Austin, TX · Software and Services

This role is for a Data Science Manager leading a team of data professionals in analytics engineering and experimentation. The focus is on partnering with ML engineering leaders to deliver data analytics, drive experimentation, and optimize performance across business goals. While the role interacts with ML, its core craft is data science and analytics leadership, not direct AI/ML model building.

What you'd actually do

  1. Hire, grow, and lead a high-caliber team of data professionals spanning analytics engineering and experimentation
  2. Guide team members in delivering data/business analytics, data storytelling, architecture guidance, and performance optimization
  3. Ensure data integrity, accessibility, and consistency across systems by overseeing data transformation and integration from multiple sources
  4. Design and implement robust monitoring, alerting, and health check programs to ensure sustained data and model performance at scale
  5. Drive experimentation and statistical analysis to uncover actionable insights and inform decision-making across business domains

Skills

Required

  • Bachelor's degree in Computer Science, Statistics, Applied Math, Engineering, or a related field
  • 7+ years of hands-on experience in data science, analytics engineering, or similar technical analytics roles
  • 2+ years of experience leading and mentoring data-focused teams
  • Proficiency in SQL and at least one programming language (e.g., Python, R)
  • Strong background in experimentation design, causal inference, and statistical analysis
  • Experience working with large-scale data pipelines and production-level analytics systems
  • Proven ability to drive measurable business impact through data and automation

Nice to have

  • Experience with building robust monitoring systems to track data and model health in production
  • Expertise in data storytelling and creating stakeholder-ready visualizations and narratives
  • Ability to connect insights to business outcomes and influence strategy
  • Familiarity with modern data platforms (e.g., Spark, Snowflake, Airflow) and ML Ops tooling
  • Passion for fostering inclusive team environments and valuing diverse perspectives
  • Clear communicator who can translate complex technical concepts into actionable business language

What the JD emphasized

  • lead and mentor data-focused teams
  • large-scale data pipelines and production-level analytics systems
  • measurable business impact through data and automation