Senior Data Scientist - Experimentation

Apple is building the next generation of ads experimentation capabilities, and this role focuses on designing and delivering a new experimentation platform for the Ads Platform organization. As a Senior Data Scientist, you will combine rigorous statistical thinking with scalable data engineering to support privacy-committed, data-driven feature launches that connect Apple users and advertisers.

The position is based in New York, NY (onsite), with a base pay range of USD 184,700 to 277,600 per year. The role requires a minimum of 5 years of relevant experience and an advanced degree in a quantitative field.

What you will do

• Design and build a new generation of experimentation platform for the Ads Platform organization.
• Use leading-edge technologies to enable safe, data-driven launches while delivering on Apple’s privacy commitment through experimentation.
• Partner with stakeholders and data scientists to design experiments and statistical methods that reflect marketplace dynamics and support reliable treatment effects.
• Provide technical guidance to engineers and scientists on statistical techniques, experiment design, and data engineering.
• Run rigorous, end-to-end analyses using SQL, Python, and statistical methods to uncover insights and improve treatment effect estimates.
• Lead the design, development, and maintenance of scalable, reliable data pipelines.
• Implement robust data quality checks, monitoring systems, and data lineage tracking.
• Advocate best practices for data engineering.
• Work directly with stakeholders, including senior leaders, to identify, scope, and prioritize high-impact questions.
• Mentor junior engineers and scientists on the team.

Required qualifications

• Advanced Degree in Computer Science, Statistics, Applied Math, or a related field.
• 5+ years of experience in software and data science or statistics, with an in-depth understanding of SQL and causal inference.
• Experience in SDLC is preferred.
• Direct experience conducting analysis to uncover insights and improve treatment effect estimates using SQL, Python, and statistical methods.
• Experience with A/B testing infrastructure and methodologies, including a deep understanding of assumptions behind randomized control trials.
• Familiarity with causal machine learning tools and technologies.
• Strong grounding in observational causal techniques such as regression, propensity score matching, Diff in Diff, regression discontinuity, and instrumental variables.
• Experience designing and analyzing controlled experiments or counterfactual causal inference studies to estimate incremental and long-term intervention impact.
• Proficiency in SQL and PySpark or Scala to conduct analyses and build data products such as pipelines and dashboards for automated experiment reporting at scale.
• Skilled at operating in a cross-functional organization and working through ambiguous, complex problems to produce clear, actionable takeaways.
• Excellent communication, social, and presentation skills.

Tools and technologies

• SQL, Python, statistical methods, PySpark, Scala
• A/B testing
• Causal machine learning tools and technologies
• Regression, propensity score matching, Diff in Diff, regression discontinuity, instrumental variables
• SDLC, data pipelines, dashboards

Compensation and benefits

• Medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Discretionary bonuses or commission payments, and relocation (if eligible)
• Participation in Apple’s discretionary employee stock programs, including eligible discretionary restricted stock unit awards and eligibility to purchase Apple stock at a discount via the Employee Stock Purchase Plan

Note: Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.