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Data Scientist, AI/ML Model Quality

Apple· Software and Services
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Hybrid (Austin, TX)Full-timePosted 2mo ago

About the role

Data Scientist focused on AI/ML model quality and data integrity across Wallet, Payments, and Commerce. Builds validation frameworks, observability metrics, and monitoring pipelines to ensure models are trained, evaluated, and deployed on high-quality data. Collaborates with ML Engineering, Data Engineering, Privacy, and Legal to surface actionable insights and ensure trustworthy data for ML and GenAI systems.

Responsibilities

  • Curate, analyze, and maintain gold-standard ground-truth datasets for model evaluation and continuous validation across both ML and GenAI systems.
  • Audit training data for systemic bias and fairness gaps prior to model deployment; establish ongoing analytical checks to catch bias introduced by data drift over time.
  • Define, track, and report key data quality metrics — completeness, accuracy, timeliness, validity — for engineering and leadership audiences.
  • Design and define automated data quality rules and thresholds, partnering with Data Engineering to ensure these checks are integrated into model development and CI/CD workflows
  • Define and own ML observability metrics — model performance, output distributions, training-serving skew, silent degradation and feature drift — translating raw production signals into actionable insights for engineering and product teams.
  • Design and develop observability dashboards and reporting workflows that give stakeholders a consistent, real-time view of model health across both conventional ML and GenAI systems.
  • Define and analyze telemetry across GenAI workflows, tracking quality signals such as output coherence, latency, task completion rates, and regression patterns.
  • Identify degradation patterns and domain-specific failure modes in GenAI systems through systematic telemetry analysis, translating findings into concrete recommendations for model and data teams.

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 stakeholders.

Skills

Required:Ground-truth data curationData quality metricsData quality rule designML observabilityTelemetry analysisDashboardingStatistical methods

Benefits

  • Apple seeks to promote equal opportunity and inclusive workplace culture; comprehensive accessibility and accommodations policies included in job postings
Last updated Sep 18, 2026. Interested? Apply on the company site.

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