Sr. Analyst, Product Analytics

Responsibilities

• Support the planning and rollout of large-scale testing strategies, including segmentation, global holdouts, and longitudinal studies.
• Develop and deploy machine learning models 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.
• Design and analyze experiments across A/B testing, multivariate testing, frequentist frameworks, and Bayesian experimentation.
• Design multi-dimensional test structures and interpret results across user segments.
• Mitigate validity threats including SRM, contamination, and multi-exposure through stratified sampling and CUPED adjustment.
• Perform causal impact measurement and competitor analysis using synthetic control models, difference-in-differences, interrupted time series, Bayesian structural time series models, seasonal-trend decomposition, and rolling regression.
• Build and maintain dashboards using Tableau/Domo to integrate ETL processes.
• Troubleshoot technical configurations, manage data pipelines, validate metrics, and support experimentation scalability.
• Utilize digital user behavior and monetization analytics, including online customer journeys, website conversions, payment-related user interactions, retention analysis, churn modeling, and marketing ROI.
• Apply data mining and behavioral segmentation techniques to uncover patterns that drive product, marketing, and revenue optimization decisions.
• Utilize 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.

Requirements

• A Master’s degree in Business Analytics, or related field, or equivalent experience.
• Three (3) years of experience developing and deploying machine learning models for user segmentation, behavior prediction, and fraud detection.
• Experience with Random Forest, Gradient Boosted Trees (XGBoost), Logistic Regression, K-Means, DBSCAN, Latent Class Analysis, and association rule mining.
• Experience with A/B testing, multivariate testing, frequentist frameworks, and Bayesian experimentation.
• Experience with multi-dimensional test structures and interpreting results across user segments.
• Experience with mitigating validity threats including SRM, contamination, and multi-exposure through stratified sampling and CUPED adjustment.
• Experience with causal impact measurement and competitor analysis using synthetic control models, difference-in-differences, interrupted time series, Bayesian structural time series models, seasonal-trend decomposition, and rolling regression.
• Experience with building and maintaining dashboards using Tableau/Domo to integrate ETL processes.
• Experience with troubleshooting technical configurations, managing data pipelines, validating metrics, and supporting experimentation scalability.
• Experience with digital user behavior and monetization analytics, including online customer journeys, website conversions, payment-related user interactions, retention analysis, churn modeling, and marketing ROI.
• Experience with data mining and behavioral segmentation techniques to uncover patterns that drive product, marketing, and revenue optimization decisions.
• Experience with 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.

Qualifications

None specified.

Skills

• Data Mining
• Behavioral Segmentation
• User Behavior Analytics
• Fraud Detection
• Machine Learning
• Experimentation Design
• Bayesian Methods
• Time Series Analysis
• Customer Journey Analytics
• Cloud Data Platforms

Benefits

Not specified.

Pay

$175,013.00 – $248,900.00/year

Schedule

Not specified.