STANLEY AROCGIARAJ

Built a demand sensing and reporting platform for a major Canadian dairy client, cutting manual efforts by 40%. Developed over 10 KPI dashboards using Power BI, SQL, Databricks, and Snowflake, boosting forecast accuracy by 12% across 1,000+ SKUs. Automated data pipelines to slash report turnaround times by 35% and drove $1 million in annual operational savings.

Analyzed customer and campaign insights from datasets exceeding 50 million records to drive commercial strategies. Automated reporting dashboards with SQL, Python, R, and R Shiny, reducing manual team efforts by 30%. Executed customer segmentation and A/B testing across 100+ annual campaigns, raising conversion rates by 20% and planning accuracy by 25%.

Leveraged massive customer datasets of over 50 million records to extract actionable marketing insights. Built automated reporting frameworks using Python, SQL, and R Shiny to boost team efficiency by 30%. Managed trend and performance analysis for 100+ campaigns yearly, optimizing conversion rates by 20% through precise audience segmentation.

Conducted comprehensive credit risk evaluations for over 50,000 customer accounts. Developed statistical application scorecards using logistic regression models, which improved default risk identification accuracy by 22%. Maintained proactive risk dashboards for recovery teams to reduce overall default exposure.

Generated weekly and monthly retail performance reports tracking over 1 million transactions for the North American market. Enhanced data governance and validation to lift overall reporting quality by 15%. Performed market basket and trend analysis, yielding a 10% lift in product uptake and campaign impact.

Completed an advanced graduate degree focusing on deep statistical methodologies, data modeling, and mathematical theory. This academic foundation provided the theoretical framework for their subsequent 12+ year career in predictive analytics, logistic regression modeling, time series forecasting, and advanced business intelligence.

Earned an undergraduate foundation centered on core statistical principles, data analysis, probability, and research methodologies. This program established the mathematical and analytical fundamentals required for entering the data domain as a Business Analyst and translating complex numbers into business insights.