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About the Role
Contribute to Machine Learning and Generative AI technologies by building and maintaining intelligent systems, validation frameworks, synthetic data generation models, and monitoring pipelines to ensure data integrity for AI systems at scale. Your work will ensure that every model is trained, evaluated, and deployed on trustworthy data, impacting hundreds of millions of users.
Responsibilities
- Build and maintain intelligent systems, validation frameworks, synthetic data generation models, and monitoring pipelines.
- Own the health of the data ecosystem that underpins ML and GenAI features across Wallet, Payments, and Commerce.
- Define and analyze observability metrics.
- Build models to calibrate and measure robustness.
- Lead telemetry analysis across GenAI workflows.
- Ensure financial features are built on the highest-quality data.
- Translate raw quality signals into insights that drive real decisions.
- Present complex ML telemetry using data visualization and dashboarding tools.
Requirements
- A Bachelor's degree with exceptional hands-on experience in ML/AI model quality or applied research, or an 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 defining and tracking ML model health metrics in production (model performance monitoring, feature drift detection, and observability instrumentation).
- 3+ years of experience in Machine Learning, 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), common ML frameworks like PyTorch, 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 with GenAI or LLM systems, including common quality failure modes, output evaluation approaches, and telemetry instrumentation.
- Strong communication skills to translate complex data quality findings and model health risks into clear, actionable insights for both engineering and non-technical stakeholders.
Skills
- Machine Learning
- Generative AI
- Data Quality
- Model Quality
- Statistical Rigor
- Production Systems
- Data Engineering
- Privacy
- Legal
- Observability
- Telemetry Analysis
- Python
- Pandas
- NumPy
- Scikit-learn
- PyTorch
- SQL
- PySpark
- Spark
- Distributed SQL
- Hypothesis Testing
- Distribution Analysis
- Data Drift Detection
- Statistical Process Control
- GenAI
- LLM
- Data Visualization
- Dashboarding
- LLM Evaluation Frameworks
- LLM-as-a-judge
- Bayesian Approaches
- Causal Graph-based Approaches
- Synthetic Data Generation
- Confidence Calibration
- Uncertainty Quantification
- ML Monitoring Platforms
- ML Observability Platforms
- MLflow
- Weights & Biases
- Privacy-constrained Data
- Regulatory Compliance Frameworks
- GDPR
- DMA
- Financial Services
- Fintech
- Consumer Payment Products
Experience Level
- 3+ years of experience in defining and tracking ML model health metrics in production
- 3+ years of experience in Machine Learning, data science or a closely related analytical role
Education Level
- Bachelor's degree with exceptional hands-on experience in ML/AI model quality or applied research
- M.S. or Ph.D. in Machine Learning, Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field
About the Company
- We are defining what exceptional data quality looks like for machine learning across Wallet, Payments, and Commerce.
- We believe that trustworthy data is the foundation of every great model.