Job Description

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 Services Product 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.

Description

Our team is looking for a Senior Data Scientist to own and drive data-driven strategy and deliver scalable ML and experimentation solutions for Apple Media Services like Apple Music, Apple TV, Podcasts, Fitness+, and more. As a key member of our diverse and multi-faceted organization, you will have the rare and exciting opportunity to work with datasets of unique magnitude, richness, and dedication to customer privacy that will frequently require innovative approaches. You will work collaboratively with partners across Business, Marketing, Product, Content, and Engineering daily to deliver material customer and business value. sponsors to lead and advise their roadmap and product strategy, influencing cross-functional priorities at a senior level

Preferred Qualifications

• Master's or PhD in a quantitative field
• Experience productionizing ML models in collaboration with engineering teams
• Experience with LLMs and GenAI applications in a production or customer-facing context
• Experience with distributed computing frameworks like Spark
• Experience in media or entertainment industry
• Passion for film, television, and/or music domains

Minimum Qualifications

• Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or related field
• 7+ years experience extracting insights from large datasets, employing programming languages including Python and SQL
• 7+ years experience employing statistical methods to tackle business problems related to classification, segmentation, forecasting, and customer lifetime value
• Demonstrated experience architecting and scaling experimentation platforms, including hypothesis testing, metric tracking, and experimentation strategy
• Strong expertise in causal inference methods including synthetic control, diff-in-diff, propensity score matching, and regression discontinuity design
• Proven track record of building and deploying ML models that directly drive business decisions and product outcomes
• Demonstrated ability to set technical direction and influence cross-functional roadmaps at a senior level
• Strong interpersonal and communication skills with ability to translate complex technical concepts for non-technical stakeholders