San Mateo, CA30+ days ago
Requires a Master's degree in Business Analytics, or related field or equivalent, and three (3) years of experience developing and deploying machine learning modeling for user segmentation, behavior prediction, and fraud detection using Random Forest, Gradient Boosted Trees (XGBoost), Logistic Regression, K-Means, DBSCAN, Latent Class Analysis, and association rule mining; designing and analyzing experiments across A/B testing, multivariate testing, frequentist frameworks, and Bayesian experimentation; designing multi-dimensional test structures and interpreting results across user segments; mitigating validity threats including SRM, contamination, and multi-exposure through stratified sampling and CUPED adjustment; performing causal impact measurement and competitor analysis using quasi-experimental methods including Synthetic Control Models, and time-series techniques including Difference-in-Differences, Interrupted Time Series, Bayesian Structural Time Series models, Seasonal-Trend Decomposition, and Rolling Regression; building and maintaining dashboards using Tableau/DOMO to integrate ETL processes; troubleshooting technical configurations, managing data pipelines, validating metrics, and supporting experimentation scalability; utilizing digital user behavior and monetization analytics, including online customer journeys, website conversions, payment-related user interactions, retention analysis, churn modeling, and marketing return on investment (ROI); applying data mining and behavioral segmentation techniques to uncover patterns that drive product, marketing, and revenue optimization decisions; and utilizing Python, R, SQL, Adobe Analytics, Customer Journey Analytics, and cloud-based data platforms including Databricks, Data Ocean, Redshift, AWS S3, MongoDB, data catalog and Postgres to manage and analyze large-scale datasets. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world.