Projects
Fraud Detection & Data Analysis
Analyzed 1.49M+ transaction records to identify anomaly patterns and improve data quality for downstream modeling
Project Name
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Warehouse Optimization
Developed scenario-based models that increased projected profit by 450% through optimized allocation strategies.
Project Name
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Fraud Detection & Data Analysis
Analyzed 1.49M+ transaction records to identify anomaly patterns and improve data quality for downstream modeling
Public Transit Performance Analytics
Analyzed 210K+ operational records to evaluate performance trends and support data-driven decision-making
Project Name
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Customer-Centric Marketing Simulation
Analyzed MV and CLV across scenarios to identify strategies that maximize long-term customer value and business performance
Overview
This project focused on analyzing large-scale transaction data to identify patterns related to fraud and anomalies
Problem
Raw transaction data contained inconsistencies and noise, making it difficult to detect meaningful patterns and support reliable modeling
Approach
• Processed and analyzed 1.49M+ transaction records using Python (pandas, NumPy)
• Performed exploratory data analysis to detect anomalies and unusual patterns
• Cleaned and transformed data to improve consistency and usability
Tools
Python (pandas, NumPy) / AMPL
Impact
Improved data quality and structure, enabling more reliable analysis and model performance in downstream tasks
Overview
In this project, I analyzed WMATA bus transit performance data to understand operational efficiency and identify improvement opportunities.
Problem
Transit systems require continuous monitoring of performance metrics to identify inefficiencies and improve service quality
Approach
• Analyzed 210K+ records across multiple routes and time periods using SQL
• Identified trends in on-time performance and service variability
• Built Tableau dashboards for ongoing monitoring and reporting
Tools
SQL, Tableau
Impact
Enabled structured performance tracking and provided insights to support operational decision-making and optimization
Overview
This project focused on optimizing warehouse-to-store allocation decisions to improve profitability.
Problem
Inefficient allocation strategies led to suboptimal profit outcomes and resource utilization
Approach
• Built scenario-based optimization models to evaluate alternative allocation strategies
• Compared multiple configurations to identify profit-maximizing solutions
• Translated model results into actionable business recommendations
Tools
Optimization modeling, Excel / AMPL
Impact
Achieved a 450% increase in projected profit, demonstrating the value of data-driven optimization in operational decision-making
CRM Analytics
Overview
In this project, I evaluated marketing strategies using customer-centric metrics such as market value (MV) and customer lifetime value (CLV)
Problem
Marketing strategies often improve short-term performance but may not maximize long-term customer value, making it difficult to identify the most effective approach
Approach
• Analyzed simulation reports and performance graphs across multiple scenarios
• Compared MV and CLV under different strategic decisions
• Evaluated trade-offs between short-term gains and long-term customer value
• Identified strategies that maximize overall business performance
Tools
Data analysis, simulation reports (Excel-based)
Impact
Identified strategies that improved both MV and CLV, demonstrating how customer-centric metrics can guide more effective and sustainable marketing decisions
