Staff Data Scientist - Experimentation

Apple Apple · Big Tech · New York City Metro Area +1 · Software and Services

Staff Data Scientist role focused on building and supporting an ads experimentation platform using machine learning and statistical methods. The role involves designing experiments, analyzing data, developing scalable tools, and collaborating with cross-functional teams to drive feature launches while ensuring user privacy. Requires strong SQL, Python, and causal inference skills, with experience in A/B testing infrastructure and data pipelines.

What you'd actually do

  1. Design and build a new generation of experimentation platform for the Ads Platform organization.
  2. Apply leading-edge technologies to enable safe and data driven launch of features that help connect Apple users and advertisers while delivering on Apple's privacy commitment through experimentation.
  3. Collaborate with stakeholders and data scientists to design experiments and statistical methods that account for the marketplace dynamics and yield reliable treatment effects.
  4. Provide expert technical guidance to engineers and scientists on statistical techniques, experiment design and data engineering.
  5. Conduct rigorous, end-to-end analyses using SQL, Python, and statistical methods to uncover insights and improve treatment effect estimates.

Skills

Required

  • SQL
  • Python
  • Causal inference
  • A/B testing
  • Data pipelines
  • Statistical methods
  • Experiment design
  • Pyspark or Scala

Nice to have

  • Marketplace experimentation
  • Observational causal techniques (regression, propensity score matching, Diff in Diff, regression discontinuity, instrumental variables)
  • Measuring advertiser value from campaigns or algorithmic changes
  • Industry experience in SDLC
  • Advanced Degree in Computer Science, Statistics, Applied Math or related field
  • Industry experience building scalable applications

What the JD emphasized

  • 8+ years of experience in software and data science or statistics with an in-depth understanding of SQL and causal inference.
  • Experience with A/B testing infrastructure and methodologies and deep understanding of the assumptions of randomized control trials.
  • Familiarity with causal machine learning tools and technologies.

Other signals

  • experimentation platform
  • A/B testing
  • causal inference
  • data pipelines
  • ML Engineer