Data Scientist - Strategic Security Risk

The Apple Services Engineering Security team builds and provides secure systems and infrastructure that fuel Apple’s services (such as iCloud, iTunes, Siri, App Store, and Maps). As part of the ASE Security team, you will help manage the security needs of Apple’s services around the world. You will build and integrate the security controls, guardrails, and frameworks that help protect our customers data in Apple’s infrastructure. In this role, you will work with highly skilled security professionals passionate about identifying, assessing, and mitigating security risks. This role is central to the controls that protect Apple’s customers, data, and brand. You’ll have the opportunity to design security processes and technology with a truly global impact. You will work closely with engineering, threat intelligence, red team, and other security teams to identify and integrate data sources to make informed risk decisions and related security efforts. You will also play a meaningful role in collaborating with Apple’s other security teams to define and implement best practices in data and signals integration and decisioning. Minimum Qualifications: MS or BS or equivalent experience in Computer Science, Engineering, Mathematics, Statistics or a related field OR equivalent practical experience in Software or Data Engineering Proficiency in SQL and at least one programming language (Python, R, etc.), with strong statistical and experimental design foundations. Experience building distributed, high-volume data services Experience with detection engineering at scale, including managing false positive rates and detection tuning methodologies Proficiency with exploratory data analysis Knowledge of secure design principles Preferred Qualifications: Experience with Cloud Computing platforms like Amazon AWS, Google Cloud Knowledge of Data Architecture principles Familiar with AWS cloud resources (S3, EC2, RDS etc) Experience with enterprise log collection and analysis platforms (e.g., Splunk, OSQuery). Hands‑on experience with large‑scale data ecosystems (Hive, Spark, Presto, etc.) and building scalable, reproducible analytical pipelines.