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.