Building Intelligent, Responsible & Impactful Solutions
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The George Washington University for applied analytics and responsible AI work
Objective: Support athlete performance, recovery, and return-to-play decisions using data.
Approach: Built Python, SQL, and Power BI dashboards to centralize workload and recovery metrics across 50+ athletes; developed injury-risk indicators using classification techniques; standardized data capture and reporting workflows.
Outcome: Reduced weekly preparation time by ~40% and enabled data-informed, weekly return-to-play decisions.
Objective: Build an applied analytics solution to support analyst decision-making in complex, review-based environments.
Approach: Developed an award-winning, analyst-facing AI chatbot using interpretable machine-learning models (EBM) and Retrieval-Augmented Generation (RAG) to surface clear drivers behind model outputs and support case prioritization.
Outcome: Improved interpretability and reduced manual review effort by enabling faster, more confident, and auditable decisions.
Objective: Support applied analytics education through hands-on, real-world examples.
Approach: Developed reproducible analytics workflows and Tableau dashboards; supported students in applying statistical and predictive methods to marketing case studies.
Outcome: Improved case-study accuracy by ~25% and strengthened student understanding of applied marketing analytics.
Objective: Build and scale a women-focused fitness studio using data-driven decision-making.
Approach: Consolidated fragmented performance and sales data into KPI dashboards; monitored campaign metrics (CTR, CAC, conversion); conducted cohort and churn analysis to identify retention risks.
Outcome: Reduced decision turnaround time by ~30%, improved marketing efficiency by ~20%, and supported sustainable business growth.
Objective: Strengthen export planning and digital marketing performance through analytics.
Approach: Built real-time Excel dashboards for sales and export KPIs; forecasted promotion ROI to guide pricing and inventory decisions; analyzed campaign performance to inform budget allocation.
Outcome: Reduced manual reporting time by 50% and contributed to a $250K+ campaign uplift.
Objective: Support commercial and promotional decisions with sales data insights.
Approach: Standardized reporting workflows; evaluated KPIs; conducted forecasting and ROI analysis to assess promotion performance.
Outcome: Enabled scalable, reproducible reporting and supported data-driven promotional planning.
Built an interpretable fraud-analysis assistant to help analysts understand why transactions are flagged. FraudLens combines a surrogate Explainable Boosting Machine (EBM), SHAP explanations, and a TF-IDF–based RAG knowledge layer to translate model behavior and rule-based fraud signals into clear, human-readable narratives for faster, auditable review workflows.
Developed interpretable models to evaluate and remediate bias in decision outcomes. Used EBM and SHAP with fairness metrics (AIR) to balance predictive performance with transparency and responsible model use.
Built a CECL-aligned credit risk framework using macroeconomic indicators (GDP, unemployment, home prices, delinquency rates) to estimate lifetime expected credit losses. Applied ARIMAX models, scenario analysis, and backtesting to balance predictive accuracy with interpretability and regulatory alignment.
Code and data not publicly available due to academic and data constraints.
Built an end-to-end SQL and Python pipeline on AWS with Power BI dashboards to identify demand patterns, underserved regions, and expansion opportunities using store performance and geographic insights.
Forecasted daily bike demand using trip and weather data to support planning and resource allocation. Applied PCA and regression models with cost-based evaluation; LASSO achieved RMSE = 4.40 and R² = 0.85.
Experienced working with cross-functional teams to deliver analyst- and stakeholder-ready solutions.
Approaches complex problems with structured analysis and practical, data-driven thinking.
Translates technical analysis into clear insights that support decision-making.
Demonstrated leadership through entrepreneurship, mentoring, and applied analytics projects.
The George Washington University School of Business · Fall 2025
Awarded for an outstanding Business Analytics Practicum project focused on building an interpretable, analyst-facing AI system for decision support.
Issued by Google | 2024
Mastered analytics fundamentals, audience segmentation, and reporting insights.
Issued by Google | 2024
Learned data manipulation, visualization, and statistical modeling using R.