Senior Product Data Scientist in Causal Inference and Ml, App Store

Apple Apple · Big Tech · Culver City · Software and Services

Senior Product Data Scientist role focused on driving data-driven strategy and delivering ML and experimentation solutions for Apple's App Store. The role involves applying advanced ML causal inference techniques, developing and deploying ML models for customer insights, and leveraging LLMs/GenAI tools. It requires strong expertise in causal inference, experimentation frameworks, and influencing cross-functional roadmaps.

What you'd actually do

  1. Own and drive the data science strategy for product, business, and creative teams working on Apple's Services.
  2. Architect and scale experimentation frameworks to drive org-wide testing velocity, including design, measurement, and interpretation of A/B tests and observational studies.
  3. Apply advanced ML causal inference techniques including synthetic control, metalearning, and counterfactual modeling to measure product impact in non-randomized settings.
  4. Develop and deploy ML models for customer scoring, segmentation, and lifetime value estimation to drive growth and acquisition strategy.
  5. Leverage LLMs and GenAI tools to develop innovative solutions to content and customer experience challenges, augmenting data science workflows at scale.

Skills

Required

  • Python
  • SQL
  • statistical methods
  • classification
  • segmentation
  • forecasting
  • customer lifetime value
  • experimentation platforms
  • hypothesis testing
  • metric tracking
  • experimentation strategy
  • causal inference methods
  • synthetic control
  • diff-in-diff
  • propensity score matching
  • regression discontinuity design
  • ML models
  • business decisions
  • product outcomes
  • technical direction
  • cross-functional roadmaps
  • interpersonal skills
  • communication skills
  • translate complex technical concepts

Nice to have

  • Master's or PhD in a quantitative field
  • productionizing ML models
  • LLMs
  • GenAI applications
  • Spark
  • Marketing Mix Models
  • matched market testing

What the JD emphasized

  • Strong expertise in causal inference methods including synthetic control, diff-in-diff, propensity score matching, and regression discontinuity design.
  • Proven track record of building and deploying ML models that directly drive business decisions and product outcomes.

Other signals

  • Develop and deploy ML models for customer scoring, segmentation, and lifetime value estimation
  • Leverage LLMs and GenAI tools to develop innovative solutions
  • Apply advanced ML causal inference techniques