We are hiring Data Scientists across multiple teams and areas of focus within Apple Ads. Our hiring process is designed to match candidates to the teams and problem spaces where they can have the greatest impact. Depending on your area of focus, you may contribute to one or more of the following domains: - Product and Marketplace Insights: Define measurement frameworks, design experiments, evaluate product and advertiser outcomes, and identify marketplace opportunities that influence product strategy and decision-making. Analyze large-scale datasets to identify opportunities, explain business outcomes, and influence decisionsDesign and evaluate experiments to understand the impact of products, features, and marketplace changesDevelop statistical, machine learning, forecasting, optimization, or econometric models to solve business problemsDefine metrics and measurement frameworks that improve visibility into product and business performancePartner closely with Product, Engineering, Sales, Finance, Marketplace, and Leadership teamsCommunicate analytical findings and recommendations to both technical and non-technical audiencesTranslate ambiguous business questions into structured analytical approachesInfluence product, business, and strategic decisions through data-driven insights and recommendationsContribute to a culture of analytical rigor, experimentation, and continuous learningRequirements Bachelor's degree in Statistics, Economics, Mathematics, Computer Science, Engineering, Data Science, or a related quantitative discipline, or equivalent practical experienceExperience in Data Science, Analytics, Machine Learning, Quantitative Research, Business Analytics, or a related fieldStrong SQL skills and experience working with large-scale, complex datasetsStrong Python programming skills and experience with common analytical libraries, including an understanding of code structure, testing, reproducibility, and scalable analytical workflowsStrong foundation in statistics, experimentation, causal inference, and analytical problem solvingExperience developing statistical, machine learning, econometric, forecasting, or optimization models to solve business problemsExperience translating analytical findings into business recommendationsAbility to communicate effectively with technical and non-technical stakeholdersExperience operating in ambiguous, fast-moving environmentsNice-to-haves
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