• Partner with Product Managers and Machine Learning teams to define and monitor key success metrics for ML-driven product features. • Design, analyze, and interpret A/B tests and other experiments to evaluate product and model changes with statistical rigor. • Analyze large-scale product and performance data to identify trends, anomalies, and drivers of business outcomes. • Support ML teams by developing analyses and metrics that help quantify model performance, traffic quality, and downstream impact. • Build and maintain dashboards and reports that enable transparent, self-service monitoring of product and ML performance. • Develop reusable SQL queries, analytical notebooks, and lightweight frameworks to streamline recurring analyses. • Investigate unexpected changes in product or business performance and clearly communicate findings and recommendations. • Collaborate with Data Scientists and Data Engineers to ensure data quality, metric consistency, and reliable instrumentation. • Document analytical approaches, assumptions, and methodologies to support reproducibility and shared knowledge.