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Data Scientist, AI/ML Model Quality

Apple· Software and Services
Apply on company site
Hybrid (New York City, NY), Hybrid (Austin, TX)$150.4k - $277.6kfull-timePosted 2mo ago

About the role

Data Scientist focused on AI/ML model quality and data integrity for ML and GenAI systems across Wallet, Payments, and Commerce. Build and maintain validation frameworks, observability metrics, and monitoring pipelines to ensure training, evaluation, and deployment use trusted data. Partner with ML Engineering, Data Engineering, Privacy, and Legal to surface actionable insights and drive decisions that maintain high-quality data for models reaching hundreds of millions of users.

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.
  • 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.

Skills

Required:PythonSQLBig data / distributed computingStatisticsML observabilityGenAI / LLM awarenessCommunication
Nice to have:Data visualization

Benefits

  • Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, reimbursement for certain educational expenses including tuition.
  • Discretionary bonuses or commission payments as well as relocation, at the employer’s discretion.
  • Apple shareholder opportunities through discretionary employee stock programs and Employee Stock Purchase Plan.
Last updated Sep 18, 2026. Interested? Apply on the company site.

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