Developed a real-time dashboard on Looker for the marketing team to track social media campaigns’ performances in comparison to team’s planned targets to lead generation, reducing response time in strategies change from monthly to a weekly basis. Conducted a comprehensive analysis collecting Meta ads data through API that revealed a discrepancy between the peak hours of client access and the available budget. Utilized this data pipeline and visualizations to create new campaign approaches to address this disparity, ultimately leading to a 10% improvement in new leads generation. Contributed to the development of a logistic regression model in Python to assess the likelihood of house sales based on property characteristics, reaching up to 80% accuracy.

Implemented efficient data cleaning processes using Python, resulting in up to 20% time savings during monthly routines. Developed comprehensive reports and presentations for stakeholders, effectively communicating insights on money spent and clients’ health insurance usage. Identified trends in the company’s health insurance bill and forecasted usage, enabling clients to manage their budgets effectively throughout the year.

Conducted in-depth analysis on retention, churn, and user behavior in a SaaS company. Utilized cohort analysis methodologies to measure and visualize customers' subscription and financial retention rates, and defined clusters of customers according to critical business variables, leading to the identification of new modules of consumption. Automated the feedback collection process from support tickets through Zapier, enhancing the product team's tool and increasing the redirection of users' requests for improvements in the platform by 50%. Collaborated with the product team in several new feature discovery processes, providing data-driven insights to support development prioritization. Actively contributed to the development of data governance by creating data dictionaries, monitoring conformity, and implementing data quality processes.