Sr. Data Analyst with experience in time-series analysis, forecasting, data visualization, and statistical modeling. Skilled in R, Python, SQL, Tableau, QuickSight, and machine learning techniques, with experience applyi…
Sr. Data Analyst with experience in time-series analysis, forecasting, data visualization, and statistical modeling. Skilled in R, Python, SQL, Tableau, QuickSight, and machine learning techniques, with experience applying these tools to energy usage forecasting, member retention analysis, and regulatory risk assessment.
• Leveraged time-series analysis techniques in R, including ARIMA and Holt-Winters models, to forecast member growth and revenue. Achieved a 96% accuracy to aid financial planning for a budget exceeding $20 million.
• Automated data extraction using MSSQL and developed electricity usage forecasting models using regression methods in Python, significantly improving prediction accuracy by 28% compared to conventional methods.
• Created a Tableau dashboard to provide real-time status tracking of 200K+ members, enhancing operational visibility and facilitating informed decision-making.
• Crafted interactive QuickSight visualizations to segment members based on residency, membership duration, and contract expiry. Identified 4K+ members at high cancellation risk to enable proactive retention strategies.
Leveraged time-series analysis techniques in R, including ARIMA and Holt-Winters models, to forecast member growth and revenue.
Achieved a 96% accuracy to aid financial planning for a budget exceeding $20 million.
Automated data extraction using MSSQL and developed electricity usage forecasting models using regression methods in Python, significantly improving prediction accuracy by 28% compared to conventional methods.
Created a Tableau dashboard to provide real-time status tracking of 200K+ members, enhancing operational visibility and facilitating informed decision-making.
Crafted interactive QuickSight visualizations to segment members based on residency, membership duration, and contract expiry.
Identified 4K+ members at high cancellation risk to enable proactive retention strategies.
Conducted a multi-variate analysis on 5-year electricity usage data for 1.5M households, examining location, property specifics, and consumption patterns with Python/MSSQL, contributing to an 18% growth in customer acquisition.
Analyzed over 15K+ cancellation surveys using Python/MSSQL to identify distinct churn drivers between new and established members.
Developed tailored retention strategies, leading to an 8% increase in retention rate.
Implemented various metrics including RMSE, MAE, and MAPE to assess the performance and robustness of electricity usage prediction models, ensuring high forecast accuracy and reliability for over 45K+ members.
Streamlined data collection and analysis of 1300+ supplier surveys using SQLite, achieving over 90% completion rate.
Saved 10 hours weekly in data processing time.
Created data visualization dashboards to monitor business metrics of 2K+ suppliers using Tableau, enabling rapid monitoring and analysis of regulatory risks.
Developed a random forest model in R for regulatory risk assessment, identifying key survey questions for risk mitigation based on feature importance.
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