Senior Product Data Scientist in Causal Inference and ML, App Store

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 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.

Our team is looking for a Senior Data Scientist to own and drive data-driven strategy and deliver scalable ML and experimentation solutions for our product and business teams. 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.\n\nOur team is involved in all stages of the product development lifecycle, from sizing, ideation, and prioritization, to instrumentation and measurement, AB testing, and post-launch feature evaluation and incremental impact assessment. We also work on diverse, collaborative projects around personalization, app quality and review, user segmentation, subscriptions, search, etc. and touch on many parts of the App Store business.

Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or related field.\n7+ years experience extracting insights from large datasets, employing programming languages including Python and SQL.\n7+ years experience employing statistical methods to tackle business problems related to classification, segmentation, forecasting, and customer lifetime value.\nDemonstrated experience architecting and scaling experimentation platforms, including hypothesis testing, metric tracking, and experimentation strategy.\nStrong expertise in causal inference methods including synthetic control, diff-in-diff, propensity score matching, and regression discontinuity design.\nProven track record of building and deploying ML models that directly drive business decisions and product outcomes.\nDemonstrated ability to set technical direction and influence cross-functional roadmaps at a senior level.\nStrong interpersonal and communication skills with ability to translate complex technical concepts for non-technical stakeholders.

Master's or PhD in a quantitative field.\nExperience productionizing ML models in collaboration with engineering teams.\nExperience with LLMs and GenAI applications in a production or customer-facing context.\nExperience with distributed computing frameworks like Spark.\nExperience with Marketing Mix Models and matched market testing.\n