New York City Metropolitan Area
Hello there! I hold a Master's degree in Business Analysis and Risk Management from Johns Hopkins University. Programming Languages: Python (NumPy, Pandas, Matplotlib, Seaborn), SQL, R Software: Tableau, PowerBI,Microsoft Excel Platform: Google Ads, Microsoft(Bing), SA360
Analyze paid search campaign performance using Google Analytics and Adobe Analytics to identify optimization opportunities and support client reporting. • Automated recurring reporting workflows using SQL and Excel, reducing manual reporting time by 20% and improving cross-platform data consistency for stakeholder reviews. • Monitor and optimize $800K+ monthly Google and Bing search budgets through pacing analysis, search query reviews, and account health checks to support ROAS and CPA goals. • Collaborate with cross-functional teams including strategy, analytics, and client services to evaluate campaign performance trends and develop data-driven recommendations. • Utilize Looker Studio to visualize keyword performance and search query metrics, driving continuous budget reallocation toward high-converting traffic
Utilized bid management tool-Google Ads platform to build campaigns, implement optimizations, export data for analysis, and QA campaigns Developed, managed and optimized campaign budgets, pacing, and bid adjustments using the Google Ads to ensure alignment with performance goals and business objectives Maintained monthly billing process and collaborated with the billing reconciliation team to reconcile discrepancies Delivered monthly actualization and KPI performance trackers to planning and analytics teams, enabling the creation of monthly reports for clients Analyzed historical data by using pivot table to evaluate campaign performance and developed actionable optimization recommendations for the following month
E-Commerce Marketing & Conversion Funnel Analysis (Data Analytics Project) | SQL, Funnel Analysis, Trend Analysis, A/B Testing Evaluation Conducted trend analysis on over 8 months of e-commerce traffic data (Google Search campaigns) using SQL to monitor weekly performance and evaluate marketing channel value. Built multi-step conversion funnels using advanced SQL queries (CASE WHEN and MAX aggregations) to isolate user drop-off points from Landing Page to final Purchase. Evaluated A/B test results for alternative landing pages, utilizing temporary tables to compare weekly bounce rates and session volumes to optimize bidding strategies. Consolidated key performance metrics (Total Sessions, Bounced Sessions, Bounce Rate) to provide data-driven insights for executive growth presentations.
Research on Gas Station's Inventory Replenishment Pattern | Python Jan 2023 – Mar 2023 • Applied time series analysis and regression models to uncover demand patterns and cost-saving opportunities, contributing to a 10% reduction in procurement costs • Collaborated with 3 members to leverage Pandas to predict fuel replenishment patterns with invoice data, developing comparison charts with Matplotlib to create targeted safety stock strategies, resulting in a 30% decrease in stockout risks • Monitored demand and supply efficiency by establishing a scorecard to manage warehouse performance and distribution centers to satisfy shipping needs and improve operational efficiency • Developed simulation models projecting a 12-15% efficiency boost through tank capacity expansion, supported by sensitivity analysis demonstrating a potential $50,000 annual cost savings Telecom Customer Churn Prediction | R Jan 2023 – Mar 2023 • Conducted Exploratory Data Analysis (EDA) to explore the relationship between customer churn and factors such as contract type, tenure, and monthly charges • Collaborated with a team of 3 members to develop predictive models using Logistic Regression, Support Vector Machines(SVM), and Random Forests to proactively reduce churn rates • Evaluated the performance of the models using 10-fold cross-validation and assessed key performance metrics such as accuracy, precision, recall, and F1-score • Compared the performance of the models using ROC curves and calculated the AUC values. Logistic regression emerged as the top-performing model with an AUC of 0.854 on the test set
• Conducted financial analysis to assess the financial health of corporate clients, evaluating key drivers such as capital structure (debt-to-equity ratio), profitability & loss, operational capacity, and debt repayment ability to provide strategic feedback on the financial conditions • Organized the bank's financial statements and conducted internal searches for payment data, including bank transfers, cheque payments, and credit card payments to support internal and external audits • Reviewed payment and bill settlement requests, collaborating with the budget team to verify and reconcile accounting entries created in Oracle to facilitate smooth account settlements.