ML Model Quality & Data Integrity Scientist

Apple Inc.; New York (NY); On-site; USD 130,000 - 170,000; Full time

Job summary

Apple is seeking a data scientist focused on AI/ML model quality across Wallet, Payments, and Commerce. You will own data quality health, validation pipelines, and observability metrics that underpin trusted models used by hundreds of millions of users.

You will collaborate with ML Engineering, Data Engineering, Privacy, and Legal teams to surface actionable insights and ensure models are trained, evaluated, and deployed on high-quality data.

Qualifications

• Degree with strong quantitative focus and hands-on ML quality experience.
• 3+ years in data science with emphasis on data quality and observability.
• Proficiency in Python, SQL, and large-scale data analysis.
• Experience with GenAI or LLM systems and telemetry instrumentation.

Responsibilities

• Curate and maintain gold-standard datasets for model evaluation and validation.
• Audit training data for bias and data drift prior to deployment.
• Define and report data quality metrics for engineering audiences.
• Create automated data quality rules with CI/CD integration.
• Own ML observability metrics across conventional ML and GenAI systems.
• Develop dashboards and reporting workflows for real-time model health.
• Analyze telemetry across GenAI workflows to surface actionable insights.
• Identify degradation patterns and propose actionable improvements.

Skills

Statistical analysis

Data quality

Model evaluation

Observability

Communication

Education

Bachelor's degree

MS or PhD preferred

Tools

Python

Pandas

NumPy

Scikit-learn

SQL

PySpark

Spark

Job description

Apple is seeking a data scientist focused on AI/ML model quality across Wallet, Payments, and Commerce. You will own data quality health, validation pipelines, and observability metrics that underpin trusted models used by hundreds of millions of users.

You will collaborate with ML Engineering, Data Engineering, Privacy, and Legal teams to surface actionable insights and ensure models are trained, evaluated, and deployed on high-quality data.