I specialize in extracting actionable insights from complex datasets through statistical analysis and business intelligence. My work spans rigorous hypothesis testing, root cause analysis for operational problems, and building interactive dashboards that communicate data clearly.
Statistical Analysis
Hypothesis testing (Welch's t-tests, Mann-Whitney U, Bayesian inference), experimental design, and rigorous research methodology to validate assumptions and quantify impact.
Root Cause Analysis
Systematic investigation of operational problems using data segmentation, statistical validation, and evidence-based recommendations.
Business Intelligence
Interactive dashboards (Power BI, Tableau, Looker Studio) that track revenue, identify profit opportunities, and support strategic decision-making.
Data-Driven Strategy
Translating complex technical findings into clear, actionable business recommendations that non-technical stakeholders can act on.
Statistical Methods: Hypothesis Testing • Experimental Design • Bayesian Inference • Regression Analysis
Languages: R • Python • SQL
Visualization: Power BI • Tableau • Looker Studio
Tools: SPSS • Excel • Google Apps Script
Conducted Welch's two-sample t-test on 17,400+ hourly observations to quantify weather impact on public transit demand. Delivered data-backed staffing recommendations achieving £23K/month cost savings through evidence-based dynamic scheduling.
Tech: Python • Hypothesis Testing • Statistical Validation
Impact: 38.7% demand impact quantified, operational optimization enabled
Analyzed 185,000+ e-commerce transactions testing whether $X.99 pricing increases sales. Applied Mann-Whitney U test after confirming non-normal distribution. Found significant effect for phones (+5.16% lift, p<0.001) but no effect for laptops.
Tech: Python • Mann-Whitney U Test • Non-Parametric Statistics
Impact: Clear evidence for category-specific pricing strategies
Built Power BI dashboard tracking $66.31M revenue across 4 regions. Automated invoice alerts with Google Apps Script and conducted root cause analysis identifying specific warehouses causing fulfillment delays.
Tech: SQL • Power BI • Google Apps Script • Root Cause Analysis
Impact: 60% reduction in manual work, improved data validation
Designed Beta-Binomial Bayesian model testing product density impact on conversion rates. Conducted sensitivity analysis comparing Bayesian vs Frequentist approaches. Demonstrated 98% probability of superiority.
Tech: Python • Bayesian Statistics • A/B Testing • Monte Carlo
Impact: Rigorous experimental design framework, probabilistic recommendations
Analyzed 5,000+ transactions using statistical validation to diagnose 48% return rate. Isolated defective product variant through systematic data segmentation. Delivered recommendations projected to increase profit margin by 23%.
Tech: SQL • Power BI • Root Cause Analysis
Impact: Clear identification of quality issue, strategic action plan
Statistical Research:
Nobel Prize Trends (124 years) • Coffee Shop Survey Analysis (R) • Cluster Analysis (Health Data)
Business Intelligence:
E-Commerce Superstore Dashboard • Customer Segmentation (Tableau) • Social Media Ad Performance
Data Analysis:
Demographic Analysis • College Event Feedback (NLP)
Certifications:
J.P. Morgan Quantitative Research • BCG Data for Decision Makers • Deloitte Data Analytics • Accenture Data Analytics & Visualization
I'm open to discussing data projects, research opportunities, or roles in analytics and business intelligence.
"The best insights come from asking the right questions, testing assumptions with data, and translating findings into decisions that create measurable impact."