The Data Scientist, AI/ML Model Quality role focuses on ensuring the integrity and quality of data that powers machine learning and generative AI technologies. You will build and maintain validation frameworks and monitoring pipelines to support the health of the data ecosystem across Wallet, Payments, and Commerce. This position requires collaboration with various teams, including ML Engineering and Data Engineering, to ensure that models are trained and evaluated on trustworthy data. The ideal candidate will have a strong background in data quality and model evaluation, contributing to the success of ML features that impact millions of users.

• A Bachelor's degree with hands-on experience in ML/AI model quality or a M.S or Ph.D in a related quantitative field is strongly preferred.
• 3+ years of experience in data science or a closely related analytical role.
• Proficiency in Python and SQL for complex data analysis.
• Experience querying and analyzing large-scale datasets using distributed computing frameworks.
• Solid understanding of statistical methods including hypothesis testing and data drift detection.
• Experience in defining and tracking ML model health metrics in production.
• Familiarity with GenAI or LLM systems and their quality failure modes.
• Strong communication skills to translate data quality findings into actionable insights.
• Experience with data visualization and dashboarding tools.
• Familiarity with LLM evaluation frameworks or techniques.
• Experience with Bayesian or causal graph-based approaches to synthetic data generation.
• Familiarity with confidence calibration techniques and uncertainty quantification.
• Experience with ML monitoring or observability platforms.
• Experience working with privacy-constrained data or under regulatory compliance frameworks.
• Background in financial services, fintech, or consumer payment products.

• Build and maintain intelligent systems, validation frameworks, and monitoring pipelines.
• Ensure the health of training and validation datasets for ML and GenAI features.
• Define and analyze observability metrics to surface actionable product insights.
• Lead telemetry analysis across GenAI workflows.
• Collaborate closely with ML Engineering, Data Engineering, Privacy, and Legal teams.
• Translate raw quality signals into insights that drive real decisions.
• Monitor and evaluate models to ensure they are trained on trustworthy data.
• Present complex ML telemetry using data visualization tools.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.