Built machine learning models (Logistic Regression, Random Forest, XGBoost, Collaborative Filtering) to predict season ticket conversions and customer churn. Achieved 88% recall in identifying high-potential leads, enabling targeted marketing campaigns and increasing engagement. Analyzed 1.5M+ customer records to uncover behavioral patterns, including attendance frequency, preferred sections, and streaks. Improved model performance on highly imbalanced data using bootstrapping, SMOTE, and class-weighted strategies. Collaborated with analytics stakeholders to deliver predictive insights aligned with campaign objectives under tight deadlines

Led product analysis across 3 core SaaS products, in collaboration with stakeholders, identifying drop-off points, feature adoption, funnel analysis, retention analysis and user engagement metrics. Built and launched the company’s first end-to-end revenue tracking system using dbt and BigQuery, delivering reliable ARR, MRR, and churn metrics and increasing data accuracy. Develop and maintain analytics reports using Metabase, dbt, and BigQuery, reducing manual workload and empowering teams with accessible insights. Managed and optimized data pipelines processing 1TB+ in Google Cloud, improving query efficiency and scalability for real-time analytics and experimentation.