Optimized digital marketing strategies by analyzing key metrics such as impressions, CTR, conversions, ROAS, and time-series trends, driving a 15% increase in campaign ROI. Designed Superset dashboards to monitor DV360 KPIs, track performance, and identify top- and low-performing brands, empowering teams to refine strategies and achieve better results.

Developed and deployed a customer churn prediction model using Vertex AI AutoML and Big Query and Python, leveraging 19 years of historical data. The model, built on 19M+ records, delivers 95% prediction accuracy, automating daily churn predictions to drive data-driven insights and optimizing retention strategies.

Constructed and optimized SQL queries in Big Query using Joins, Unions, Subqueries, and Window functions for revenue analysis. Conducted RFM analysis to segment customers into loyal, first-time, and other categories, driving targeted marketing strategies that boosted campaign efficiency and ROI.

Automated data pipelines with Cloud Scheduler and Cloud Functions to export DV360 and CM360 reports into Big Query, streamlining ETL processes, data modeling, enhancing data efficiency and integrity, saving over 10 hours per week.

Processed, labeled, and normalized raw data, ensuring data integrity through thorough validation and quality checks. Conducted in- depth exploratory data analysis (EDA) on large historical datasets, uncovering valuable trends and patterns that boosted forecasting accuracy by 10%. Proactively identified and corrected data anomalies, ensuring reliable insights for data-driven business strategies and enhanced decision-making. 

Built multiple interactive dashboards in Superset, Looker, and Tableau to visualize key performance indicators and model outputs, enabling stakeholders to easily interpret data-driven insights. These dashboards provided a comprehensive view of campaign performance, customer trends, and other crucial metrics, informed decision-making and optimizing strategic alignment across teams.