Data Scientist, AI/ML Model Quality

Apple Inc

Quick summary

Work type: On-site
Location: New York City, NY
Salary: $147,400–$272,100 / yr
Posted: 3 days ago
Nearby: 99+ roles within 25 mi

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Competitive pay

How this pay compares to similar roles

Similar $191k

This role $210k

$131k most similar roles pay here $287k

This role pays more than 60% of similar roles. Most pay $145,743–$236,900 — the shaded band above. At the midpoint, this role pays about $210k versus about $191k for comparable roles.

Based on 240 similar postings.

Employer

About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 1777 open roles on FindRole.

Listed pay typically runs $162,500–$272,100 across 1443 roles with salary data.

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At a glance

TL;DR · Data Scientist, AI/ML Model Quality

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As a Data Scientist specializing in AI/ML Model Quality at the senior level, you will join a team dedicated to ensuring the integrity of machine learning and generative AI systems across Wallet, Payments, and Commerce. Your daily responsibilities include curating gold-standard datasets for model evaluation, auditing training data for bias, defining key quality metrics, and designing automated validation rules in collaboration with Data Engineering teams. You will also develop observability dashboards and analyze telemetry to track the health of both conventional ML models and generative AI systems, ensuring that every feature reaches hundreds of millions of users on a high-quality data foundation. The role requires expertise in Python (Pandas, NumPy, Scikit-learn), SQL, and distributed computing frameworks like PySpark, alongside a solid understanding of statistical methods for model evaluation and observability in production environments.

Skills

Python Pandas NumPy Scikit-learn SQL PySpark Hypothesis testing Distribution analysis Data drift detection Statistical process control ML observability Tableau Apache Superset Databricks LangSmith Bayesian approaches Causal graph-based approaches Confidence calibration Uncertainty quantification MLflow Weights & Biases

What you'll do

Curate and maintain gold-standard datasets for model evaluation.
Audit training data to prevent bias before model deployment.
Define and track key data quality metrics for engineering teams.
Design automated data quality rules integrated into CI/CD workflows.
Define ML observability metrics to provide actionable insights.
Develop observability dashboards for real-time model health monitoring.
Analyze telemetry across GenAI workflows to identify degradation patterns.

What we're looking for

Bachelor's degree with 3+ years of experience in ML/AI model quality or related analytical role.
Strong proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL for data analysis.
Experience querying large-scale datasets using distributed computing frameworks like PySpark.
Solid understanding of statistical methods including hypothesis testing and data drift detection.
Familiarity with GenAI systems and common quality failure modes.
Strong communication skills to translate complex findings into actionable insights.

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