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Conducted A/B testing for an e-learning platform to understand the impact of user behavior, increasing coupon code utilization by 15%.

Implemented automated ETL processes with Kubernetes and Apache Airflow, reducing manual labor by 35 hours per month and allowing for the processing of millions of rows of data using SQL.

Implemented AWS Lambda and CloudWatch to efficiently manage and monitor data storage and processing on S3, resulting in improved data management and a 20% increase in data processing speed in the cloud.

Guided a team of interns in creating three internal dashboards, collaborating closely with marketing account managers to reduce manual work by 75%.

Refactored more than 15 SQL stored procedures by optimizing queries, adding comprehensive documentation, and adjusting business logic to align with updated marketing processes.

Developed a data pipeline using Python and SQL to process semi-structured data from 3 external REST APIs, processing over 200 data points per day, resulting in improved data management and enabling more effective analysis and reporting.

Designed architecture to ingest approximately 2TB of discussion data from Reddit and perform analysis using Databricks, resulting in a 20% increase in data processing efficiency.

Led a team of 4, implementing the Leiden clustering algorithm for discussion clustering using Python.

Automated a manual data extraction process for classifying vehicle insurance documents from multiple dealerships, improving SLA from 50 minutes to 1 minute.

Deployed a text classification model using Flask to assign IT support tickets to their respective groups, achieving 80% accuracy and reducing response time by 50%.