Senior Data Scientist - Experimentation

Apple•New York City, NY

About The Position

Apple’s Advertising Platforms group builds high-performance advertising technologies and services, including Search Ads in the App Store and Apple News, with a focus on effective advertising and user privacy. The Ads Experimentation team is seeking a hands-on Machine Learning Engineer and Statistician to develop and support a state-of-the-art experimentation platform for the Ads Platform organization. The role will use leading-edge technologies to enable safe, data-driven launches of features and help connect Apple users and advertisers while upholding Apple’s privacy commitment. The Senior Data Scientist will partner with cross-functional teams, stakeholders, and data scientists to design experiments and statistical methods that account for marketplace dynamics and produce reliable treatment-effect estimates. The role includes conducting rigorous analyses, measuring performance, building scalable tools and data products, and providing technical guidance on statistical techniques, experiment design, and data engineering. The team values observability, understandability, reliability, resiliency, simplicity, reusability, extensibility, scalability, velocity, and productivity.

Requirements

At least 5 years of experience in software and data science or statistics.
In-depth understanding of SQL and causal inference.
Experience with A/B testing infrastructure and methodologies, and a deep understanding of randomized controlled trial assumptions.
Proficiency in SQL and PySpark or Scala for analysis and building data products.
Experience building scalable applications in industry; the posting separately specifies 6+ years of industry experience building scalable applications.
Advanced degree in Computer Science, Statistics, Applied Math, or a related field.
Strong communication, social, and presentation skills.
Ability to operate in a cross-functional organization and work with stakeholders, including senior leaders.
Ability to understand ambiguous and complex problems and turn analysis into clear, actionable takeaways.
Curiosity, ownership, and willingness to seek out projects.
Desire to work in a fast-paced and challenging work environment.

Nice To Haves

Industry experience in the software development life cycle (SDLC).
Experience in marketplace experimentation.
Familiarity with causal machine-learning tools and technologies.
Knowledge of observational causal techniques, including regression, propensity score matching, difference-in-differences, regression discontinuity, and instrumental variables.
Experience measuring advertiser value from campaigns or algorithmic changes; described as a huge plus.

Responsibilities

Design and build a new generation of experimentation platform for the Ads Platform organization.
Use leading-edge technologies to enable safe, data-driven launches of advertising features while supporting Apple’s privacy commitment.
Collaborate with stakeholders and data scientists to design experiments and statistical methods that account for marketplace dynamics and yield reliable treatment effects.
Conduct end-to-end analyses using SQL, Python, and statistical methods to uncover insights and improve treatment-effect estimates.
Design and analyze controlled experiments or counterfactual causal-inference studies to estimate the incremental and long-term impact of interventions.
Build data products, including pipelines and dashboards, to automate experiment reporting at scale.
Lead the design, development, and maintenance of scalable and reliable data pipelines.
Implement data-quality checks, monitoring systems, and data-lineage tracking.
Advocate best practices for data engineering.
Work with stakeholders, including senior leaders, to identify, scope, and prioritize high-impact questions.
Translate ambiguous and complex problems into analytical approaches and communicate clear, concise takeaways that drive action.
Provide expert technical guidance to engineers and scientists on statistical techniques, experiment design, and data engineering.
Mentor junior engineers and scientists on the team.
Measure advertiser value from campaigns or algorithmic changes.