The Data Scientist, AI/ML Model Quality role focuses on ensuring the integrity of data that powers machine learning and generative AI technologies. The position involves building and maintaining intelligent systems, validation frameworks, and monitoring pipelines to uphold data quality across Wallet, Payments, and Commerce. The ideal candidate will work closely with ML Engineering, Data Engineering, Privacy, and Legal teams to define observability metrics and lead telemetry analysis. This role is crucial for maintaining the health of training and validation datasets, impacting hundreds of millions of users.

• A Bachelor's degree with exceptional hands-on experience in ML/AI model quality or applied research, 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 with a focus on data quality.
• 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 convey complex data findings to technical and non-technical stakeholders.
• 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 every model is trained, evaluated, and deployed on trustworthy data.
• Collaborate closely with various teams to define and analyze observability metrics.
• Lead telemetry analysis across GenAI workflows to ensure high-quality data.
• Own the health of the data ecosystem that underpins ML and GenAI features.
• Translate raw quality signals into actionable insights for decision-making.
• Define and track ML model health metrics in production environments.

• 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.