Jan 2023 – Aug 2025 · 2 yrs 7 mos · Hybrid

• Designed and developed data visualizations and dashboards to track business performance and key metrics.
• Extracted, transformed, and analyzed large datasets from multiple sources using SQL and Python.
• Conducted statistical analysis and hypothesis testing to support data-driven decision-making.
• Applied machine learning techniques to build classification and regression models for predictive insights.
• Automated reporting processes using Python scripts to improve efficiency and reduce manual effort.
• Created data pipelines to integrate structured and unstructured data for real-time analytics.
• Developed A/B testing frameworks to evaluate business strategies and optimize marketing campaigns.
• Performed exploratory data analysis (EDA) to identify patterns, anomalies, and business opportunities.
• Implemented advanced SQL queries for data extraction, joins, and performance optimization.
• Built customer retention models to identify at-risk customers and improve engagement strategies.
• Designed and implemented data validation processes to ensure accuracy and consistency in reporting.
• Provided business insights and recommendations based on in-depth data analysis.