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 define observability metrics and lead telemetry analysis. Your work will directly impact the quality of models that serve 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, focusing on data quality, model evaluation, or ML observability in production environments.
• Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL for complex data analysis.
• Experience querying and analyzing large-scale datasets using distributed computing frameworks like PySpark or Spark.
• 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 evaluation approaches.
• Strong communication skills to translate complex findings into actionable insights.
• Experience with data visualization and dashboarding tools like Tableau or Databricks.
• Familiarity with LLM evaluation frameworks or techniques like LLM-as-a-judge.
• 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 like MLflow or Weights & Biases.
• Experience working with privacy-constrained data or under regulatory compliance frameworks.

• Build and maintain intelligent systems, validation frameworks, and monitoring pipelines.
• Ensure that every model is trained, evaluated, and deployed on trustworthy data.
• Collaborate closely with ML Engineering, Data Engineering, Privacy, and Legal teams.
• Define and analyze observability metrics to surface actionable product insights.
• Lead telemetry analysis across GenAI workflows.
• Own the health of training and validation datasets.
• Translate raw quality signals into insights that drive real decisions.
• Monitor and maintain the quality of data ecosystems that underpin ML and GenAI features.

• 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.