Data Scientist, AI/ML Model Quality

Apple Inc.; San Diego (CA); On-site; USD 130,000 - 170,000; Full time

Job summary

Apple in San Diego is seeking a data scientist focused on ML model quality and data health. You will build validation frameworks, observability metrics, and telemetry analysis across Wallet, Payments, and GenAI features to ensure trustworthy training data and robust deployment.

The ideal candidate has a strong statistical background, experience with data drift, and production ML observability, and can translate complex quality signals into clear actions for engineering teams.

Qualifications

• A Bachelor's degree with hands-on ML/AI quality experience or MS/PhD in a quantitative field is preferred.
• 3+ years in data science with focus on data quality, model evaluation, or ML observability in production.
• Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL for data analysis and validation.
• Experience querying large-scale datasets with distributed computing frameworks like PySpark or Spark.
• Strong statistical methods knowledge including hypothesis testing and data drift detection.
• Experience defining and tracking ML health metrics in production and observability instrumentation.
• Familiarity with GenAI/LLM systems and telemetry instrumentation.

Responsibilities

• Own the health of data ecosystems underpinning ML/GenAI features across Wallet, Payments, and Commerce.
• Build validation frameworks, observability metrics, and telemetry analyses to surface actionable insights.
• Collaborate with ML Engineering, Data Engineering, Privacy, and Legal to ensure trustworthy data and models.

Skills

Python ML

SQL

Statistical methods

Model health monitoring

Data drift detection

Observability

Data storytelling

Education

Bachelor's degree

MS/PhD in ML/CS/DS/Stats

Tools

PySpark

Spark

Distributed SQL

Job description

Would you like to contribute to Machine Learning and Generative AI technologies? Are you passionate about the integrity of the data that powers AI systems at scale? Do you believe that trustworthy data is the foundation of every great model? We truly believe it is!

We are defining what exceptional data quality looks like for machine learning across Wallet, Payments, and Commerce. As a Data Scientist, AI/ML Model Quality, you will build and maintain intelligent systems, validation frameworks, and monitoring pipelines that keep our data ecosystem healthy — ensuring that every model we build is trained, evaluated, and deployed on data we can trust. Your work sits at the foundation of every ML feature that reaches hundreds of millions of users.

You’ll work at the intersection of statistical rigor and production systems, collaborating closely with ML Engineering, Data Engineering, Privacy, and Legal teams. This unique opportunity puts you at the center of ML and AI quality — owning the health of training and validation datasets, defining and analyzing observability metrics to surface actionable product insights, and leading telemetry analysis across GenAI workflows — ensuring Apple’s financial features are built on the highest-quality data, whether powering conventional ML models or the latest generative AI systems.

Description

The ideal candidate is a detail-obsessed data scientist who understands that model quality starts long before training — it starts with the data. You have strong statistical instincts, know how silent degradation and data drift manifest in production systems, and can translate raw quality signals into insights that drive real decisions.

You will own the health of the data ecosystem that underpins ML and GenAI features across Wallet, Payments, and Commerce — building validation frameworks, defining observability metrics, and leading telemetry analysis that keeps every model trained, evaluated, and monitored on data teams can trust. Your work sits at the foundation of every ML feature that reaches hundreds of millions of users.

Minimum Qualifications

• 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 Machine Learning, Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field is strongly preferred.
• 3+ years of experience in data science or a closely related analytical role, with a strong focus on data quality, model evaluation, or ML observability in production environments.
• Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL for complex data analysis, metric creation, and validation.
• Experience querying and analyzing large-scale datasets using distributed computing frameworks (e.g., PySpark, Spark, or distributed SQL).
• Solid understanding of statistical methods — hypothesis testing, distribution analysis, data drift detection, and statistical process control.
• Experience in defining and tracking ML model health metrics in production — model performance monitoring, feature drift detection, and observability instrumentation.
• Familiarity with GenAI or LLM systems, including common quality failure modes, output evaluation approaches, and telemetry instrumentation.
• Strong communication skills — ability to translate complex data quality findings and model health risks into clear, actionable insights for both engineering and non-technical stakeholde

Preferred Qualifications

• Experience with data visualization and dashboarding tools (e.g., Tableau, Apache Superset, Databricks) to present complex ML telemetry.
• Familiarity with LLM evaluation frameworks (e.g. LangSmith) 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 (e.g., MLflow, Weights & Biases, or equivalent).
• Experience working with privacy-constrained data or under regulatory compliance frameworks (GDPR, DMA).
• Background in financial services, fintech, or consumer payment products.