Karanveer Singh

Built a Python and SQL-based business forecast model processing 547,500+ daily data points across demand, competition, and seasonality for 1,500+ assets; transformed a complex multi-dimensional pricing problem into a mathematical model that drove 14% YoY revenue growth vs. 3% market benchmark. Developed automated ETL reporting pipelines in BigQuery eliminating manual data extraction, reducing deci

Mined large-scale reservation datasets using SQL and BigQuery to identify a booking mix anomaly (18% to 34% last-minute share) suppressing ADR; delivered root-cause analysis to leadership that recovered ~$180K in revenue within 3 weeks. Applied clustering and regression analysis with Finance and Marketing to model customer lifetime value across acquisition channels; findings shifted budget toward

Performed statistical analysis on 1M+ data points using Python and R, applying supervised modeling (logistic regression, panel data regression) and unsupervised clustering; achieved 85-92% accuracy, a 25% improvement over the prior baseline. Established a scalable model validation and drift-detection framework using holdout testing and performance monitoring; adopted as lab standard practice and d

Performed statistical analysis and clustering on 2 years of equipment failure data using SQL and R to identify 6 root-cause breakdown clusters; deployed IoT-based real-time monitoring that reduced breakdown frequency by 34% YoY and saved 1,100+ minutes of downtime annually. Built a centralized capacity management model using customer demand forecasts and machine utilization data across 3 plants; a

M.S. in Business Analytics from the W. P. Carey School of Business at Arizona State University, completed from 2022 to 2023.

B.E. in Electronics, Instrumentation and Control Engineering from Thapar Institute of Engineering and Technology, completed from 2014 to 2018.