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

Confirmed live in the last 24 hours

Apple

Cupertino

On-site

Posted April 1, 2026

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 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.

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 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. Our 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.

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.

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 with Marketing Mix Models and matched market testing.

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