Passionate about building robust data reconciliation systems, retail product matching engines, and anomaly detection pipelines using Python, SQL, and Power BI.
| Project | Focus Areas | Tech Stack | Repository |
|---|---|---|---|
| Retail Data Quality & Product Matching System | Multi-attribute candidate generation, pack-size mismatch constraints, release-blocking quality gates | Python, scikit-learn, RapidFuzz, pandas | retail-product-matching |
| Sales Anomaly Investigation System | Strictly causal rolling baselines, multi-SKU facility closure RCA, commercial loss vs ingestion gap separation | Python, pandas, NumPy, SciPy | sales-anomaly-investigation |
| Product Category Classification & Novelty Discovery | Leak-free product-grouped splits, physical quantity preservation, OOD recall vs auto-pass containment | Python, scikit-learn, TF-IDF, CalibratedClassifier | product-category-classification |
| Retail Data Delivery & Partner SLA Monitor | Contract schedule alignment, midnight rollover lateness, mandatory telemetry gating, monthly FRI scoring | Python, pandas, unittest | retail-data-delivery-sla-monitor |
| Textile Inventory & Sales Reconciliation System | Multi-stage material flows, independent shrinkage math, delivery challan vs invoice value reconciliation | Python, openpyxl, pandas | textile-inventory-sales-reconciliation |
