Data Scientist

Monitoring, Documentation & Communication, Business Impact. Conducted in-depth exploratory data analysis (EDA) using Pandas, NumPy, Matplotlib, and Seaborn to identify trends, patterns, and anomalies within large datasets. Designed and implemented robust data pipelines for efficient data extraction, transformation, and loading (ETL) processes using Python and SQL (MySQL). Developed and deployed a predictive churn model utilizing a combination of Logistic Regression and XG Boost algorithms. Successfully reduced customer churn by 10% and increased model accuracy by 15% through proactive customer engagement strategies. Monitored model performance continuously, identifying areas for improvement and retraining models as needed to maintain optimal performance. Developed and maintained comprehensive documentation for data pipelines, models, and analysis for effective knowledge sharing and future reference. Presented findings and recommendations to stakeholders through clear and concise reports and presentations, effectively communicating complex data insights.