Job description

Product Data Science sits within Apple Services Engineering, the org that runs Apple's content platforms end-to-end. The team builds the intelligence layer behind partner-facing analytics applications and Apple's global content charts. Recent examples of our work include a Bayesian experimentation engine that powers Product Page Optimization in App Store Analytics, and differential privacy solutions behind the Peer-Group Benchmarks feature, giving developers privacy-safe performance insights they could not get anywhere else. We stay close to the research and encourage the team to do the same, whether in Bayesian methods, privacy-preserving ML, or applied AI. There are regular opportunities to present work at internal tech talks and external conferences. We care deeply about translating research into features that give content partners materially useful insights, and help users discover more of what Apple's platforms have to offer. First-principles understanding of the methods you use: able to explain why an algorithm works, its assumptions, and where it breaks. Proficiency across multiple ML domains: supervised and unsupervised learning, deep learning, time-series modeling, and Bayesian statistics. Production-quality software engineering in Python, including reusable service design and the full deployment lifecycle. Experience taking 0-to-1 features end-to-end: problem framing, algorithm design, and production deployment. MS or PhD in Statistics, Computer Science, Machine Learning, or a related quantitative field. Candidates with equivalent industry experience will be considered. 3-5+ years of industry experience designing and deploying ML or statistical solutions in production. Experience with differential privacy, causal inference, or statistical experimentation (A/B testing, Bayesian experimentation). Familiarity with distributed data platforms and web-scale pipelines. Exposure to applied AI, LLMs, and agentic systems. Production engineering experience in Scala or Spark. You think in user outcomes, not model metrics. Communicates clearly across technical and non-technical audiences, and across time zones. Comfortable working independently and collaboratively in a geographically distributed, cross-functional org.

Requirements

Must-haves

First-principles understanding of the methods you use: able to explain why an algorithm works, its assumptions, and where it breaks.
Proficiency across multiple ML domains: supervised and unsupervised learning, deep learning, time-series modeling, and Bayesian statistics.
Production-quality software engineering in Python, including reusable service design and the full deployment lifecycle.
Experience taking 0-to-1 features end-to-end: problem framing, algorithm design, and production deployment.
MS or PhD in Statistics, Computer Science, Machine Learning, or a related quantitative field. Candidates with equivalent industry experience will be considered.
3-5+ years of industry experience designing and deploying ML or statistical solutions in production.
Experience with differential privacy, causal inference, or statistical experimentation (A/B testing, Bayesian experimentation).
Familiarity with distributed data platforms and web-scale pipelines.
Exposure to applied AI, LLMs, and agentic systems.
Production engineering experience in Scala or Spark.
You think in user outcomes, not model metrics.
Communicates clearly across technical and non-technical audiences, and across time zones.
Comfortable working independently and collaboratively in a geographically distributed, cross-functional org.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlaipythonmachine learning

Platform: apple
Effort: medium