Senior Data Scientist, Marketing Analytics

Apple Apple · Big Tech · Cupertino, CA · Operations and Supply Chain

Senior Data Scientist role focused on marketing analytics, using AI/ML to understand customer behavior, develop insights, and build predictive models for personalized marketing communications. The role involves end-to-end solution delivery, partnering with ML teams, and leveraging LLM tooling.

What you'd actually do

  1. Proactively identify business problems and analyze customer behavior to inform strategies for product purchase, upgrades, and cross-product expansion
  2. Translate analytical findings into clear visualizations and actionable recommendations for Marketing Communications and cross-functional partners
  3. Design, build, and maintain Tableau dashboards to track the impact of product launches and marketing activities on a regular cadence
  4. Develop and apply machine learning models (e.g., classification, regression, ensemble methods) to optimize marketing strategies and personalization efforts
  5. Partner with Engineering and Machine Learning teams to develop scalable data pipelines and production-ready modeling solutions

Skills

Required

  • Graduate degree in Business (quantitative focus), Statistics, Data Mining, Machine Learning, Analytics, Econometrics, Mathematics, Operations Research, Industrial Engineering, or a related field with 4+ years of experience OR Bachelor's with 6 years of relevant experience.
  • Strong foundation in machine learning methods, including classification, regression, clustering, and ensemble techniques
  • Proficiency in Python or Spark, with experience developing production-level analytical solutions
  • Advanced SQL skills, including query optimization and data modeling in Snowflake
  • Experience building and maintaining data visualization dashboards using Tableau or other visualization tools

Nice to have

  • Strong business acumen with the ability to connect analytical insights to strategic decision-making
  • Experience with advanced marketing analytics techniques (e.g., time-series regression, marketing mix modeling, multi-touch attribution)
  • Experience using LLMs and AI-assisted tooling to improve analytical productivity
  • Experience presenting analytical findings and recommendations to senior leadership
  • Experience managing end-to-end analytics projects with multiple competing priorities
  • Experience partnering with technical and non-technical teams to translate business questions into analytical frameworks
  • Experience working with incomplete or ambiguous data to deliver results in a dynamic environment

What the JD emphasized

  • end-to-end solutions
  • scalable solutions
  • LLM-based tooling
  • production-level analytical solutions
  • end-to-end analytics projects

Other signals

  • develop predictive models
  • apply advanced analytical and AI-driven approaches
  • deliver end-to-end solutions
  • build scalable solutions
  • leverage modern AI and LLM-based tooling