Apple
Summary
Services at Apple help hundreds of millions of customers get the most out of the devices they love through amazing apps, award-winning shows and movies, immersive music in spatial audio, world-class workouts and meditations, super fun games and more! The Apple Media Products Data Science & Analytics organization is passionate about developing discerning insights and machine learning solutions to help continually improve these services and accelerate growth while maintaining a strong dedication to customer privacy. As an Applied Scientist, you will be working on innovative projects at the intersection of causal inference, statistics, and machine learning to help optimize marketing channels via observational testing frameworks, counterfactual models, and lifetime value models for Services such as Apple TV+, the App Store, Apple Music, and more. As a key member of our diverse and dynamic organization, you'll have the rare and rewarding opportunity to work with datasets of unique magnitude, richness, and dedication to privacy that will frequently require innovative approaches. You'll work alongside partners across Business, Marketing, Product and Engineering daily to deliver material customer and business value. Key Qualifications
3+ years of experience in Applied Science or Data Science, preferably for an internet technology company. Familiarity with a broad range of quasi-experimental Causal Inference techniques such as diff-in-diff, panel analysis, regression discontinuity design, interrupted time series, and propensity score matching. Solid understanding of Machine Learning, statistical data analysis, and A/B tests. Hands-on experience building Marketing Mix models and validating with Matched Market tests. Proven ability to analyze different data types, including time series and heterogeneous data. Strong proficiency with SQL-based languages, Python/R, Spark, and additional large-scale data / Hadoop-ecosystem tools. Working proficiency with Git. Experience in contributing to production code bases. Ability to rapidly prototype algorithmic ideas in notebook environments and translate them into production code. Excellent communication, social, and presentation skills with meticulous attention to detail. Curious business mentality with an ability to condense sophisticated concepts and models into clear and concise takeaways that drive action. Description
Collaborate with Apple Media Products team members and partners to use Causal Inference techniques that help us optimize our Marketing efforts, focusing on Services such as Apple TV+, Apple Music, Apple Fitness+, Apple Arcade, the App Store, and Apple One. Engineer end-to-end scalable and robust Causal Inference products which provide Apple with an understanding of the health of our Services’ marketing efforts. Dive deep into large-scale data sources to uncover opportunities for Causal Inference automation, predictive methods, and quantitative modeling across Apple services. Partner with other Apple organizations on data engineering, data governance, voicing support for Causal Inference techniques, and democratizing data. Education & Experience
Minimum of a Bachelor’s degree in Statistics, Mathematics, Computer Science, Physics, Engineering, or related field. Ideally, a Master’s or PhD in a related field. About the company
Work at Apple! Join a team and inspire the work. Discover how you can make an impact: See our areas of work, worldwide locations, and opportunities for students.
#J-18808-Ljbffr
Services at Apple help hundreds of millions of customers get the most out of the devices they love through amazing apps, award-winning shows and movies, immersive music in spatial audio, world-class workouts and meditations, super fun games and more! The Apple Media Products Data Science & Analytics organization is passionate about developing discerning insights and machine learning solutions to help continually improve these services and accelerate growth while maintaining a strong dedication to customer privacy. As an Applied Scientist, you will be working on innovative projects at the intersection of causal inference, statistics, and machine learning to help optimize marketing channels via observational testing frameworks, counterfactual models, and lifetime value models for Services such as Apple TV+, the App Store, Apple Music, and more. As a key member of our diverse and dynamic organization, you'll have the rare and rewarding opportunity to work with datasets of unique magnitude, richness, and dedication to privacy that will frequently require innovative approaches. You'll work alongside partners across Business, Marketing, Product and Engineering daily to deliver material customer and business value. Key Qualifications
3+ years of experience in Applied Science or Data Science, preferably for an internet technology company. Familiarity with a broad range of quasi-experimental Causal Inference techniques such as diff-in-diff, panel analysis, regression discontinuity design, interrupted time series, and propensity score matching. Solid understanding of Machine Learning, statistical data analysis, and A/B tests. Hands-on experience building Marketing Mix models and validating with Matched Market tests. Proven ability to analyze different data types, including time series and heterogeneous data. Strong proficiency with SQL-based languages, Python/R, Spark, and additional large-scale data / Hadoop-ecosystem tools. Working proficiency with Git. Experience in contributing to production code bases. Ability to rapidly prototype algorithmic ideas in notebook environments and translate them into production code. Excellent communication, social, and presentation skills with meticulous attention to detail. Curious business mentality with an ability to condense sophisticated concepts and models into clear and concise takeaways that drive action. Description
Collaborate with Apple Media Products team members and partners to use Causal Inference techniques that help us optimize our Marketing efforts, focusing on Services such as Apple TV+, Apple Music, Apple Fitness+, Apple Arcade, the App Store, and Apple One. Engineer end-to-end scalable and robust Causal Inference products which provide Apple with an understanding of the health of our Services’ marketing efforts. Dive deep into large-scale data sources to uncover opportunities for Causal Inference automation, predictive methods, and quantitative modeling across Apple services. Partner with other Apple organizations on data engineering, data governance, voicing support for Causal Inference techniques, and democratizing data. Education & Experience
Minimum of a Bachelor’s degree in Statistics, Mathematics, Computer Science, Physics, Engineering, or related field. Ideally, a Master’s or PhD in a related field. About the company
Work at Apple! Join a team and inspire the work. Discover how you can make an impact: See our areas of work, worldwide locations, and opportunities for students.
#J-18808-Ljbffr