Senior Data Scientist - Experimentation

Apple•New York, NY

About The Position

Apple’s Advertising Platforms group supports advertising in Search Ads in the App Store and Apple News, with a focus on high-performance systems at scale and protecting 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 advertising experimentation platform. The role partners with cross-functional teams to coordinate application development and create intuitive user experiences. It also involves rigorous analysis, performance measurement, data-driven decisions, and development of scalable tools that enable quick and accurate decision-making. The successful candidate will design and build a new generation of experimentation platform for the Ads Platform organization. They will apply advanced technologies to enable safe, data-driven feature launches while supporting Apple’s privacy commitments. The role collaborates with stakeholders and data scientists to design experiments and statistical methods that account for marketplace dynamics and produce reliable treatment-effect estimates. It also provides technical guidance on statistical techniques, experiment design, and data engineering, and contributes to a culture emphasizing observability, 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.
Familiarity with causal machine-learning tools and technologies.
Knowledge of observational causal techniques such as regression, propensity score matching, difference-in-differences, regression discontinuity, and instrumental variables.
Proficiency in SQL and PySpark or Scala for analysis and building data products.
Ability to design and execute analytical approaches to ambiguous and complex problems and turn results into clear takeaways that drive action.
Ability to operate in a cross-functional organization and work directly with stakeholders, including senior leaders.
Excellent communication, social, and presentation skills.
Curious business attitude and a demonstrated ability to seek projects and take ownership.
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.
Experience measuring advertiser value from campaigns or algorithmic changes.
Advanced degree in Computer Science, Statistics, Applied Math, or a related field.
At least 6 years of industry experience building scalable applications.

Responsibilities

Design and build a new generation of experimentation platform for the Ads Platform organization.
Apply technologies to support safe, data-driven feature launches while upholding 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.
Provide technical guidance to engineers and scientists on statistical techniques, experiment design, and data engineering.
Conduct rigorous 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.
Use SQL and PySpark or Scala to conduct analyses and build data products, including pipelines and dashboards, that 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 data-engineering best practices.
Work with stakeholders, including senior leaders, to identify, scope, and prioritize high-impact questions.
Translate ambiguous and complex problems into analytical approaches and clear, concise takeaways that drive action.
Mentor junior engineers and scientists on the team.
Contribute to a collaborative team culture and support colleagues’ growth and delivery for customers.