Haruniya Rajan

Data Scientist at Kohls’s: Built a predictive forecasting model for Kohl’s, integrating distribution, marketing data and customer behavior trends to optimize inventory levels and inform pricing strategies. Enhanced forecast accuracy by 5% through iterative model refinement, aiding data-driven financial decision-making. Optimized Azure Databricks (PySpark) scripts for Kohl’s forecasting workflows, streamlining execution by 10%, enabling year-ahead predictions, and facilitating continuous model monitoring. Collaborated with the DemandBrain product team to improve code quality by conducting thorough quality assurance testing (including A/B testing), which resulted in a 3% decrease in inventory costs for Kohl’s. ETL Tester at Prudential Financial: In the context of IFRS17 compliance, architected a robust data model (10+ entities) to manage customer transactions and insurance policies. Applied industry-standard data modeling principles, ensuring adherence to regulatory reporting requirements and reducing downstream transformation errors by 25%. Developed and executed 50+ comprehensive ETL test cases within a complex SAS Studio/Azure environment, safeguarding the integrity of financial risk data. Identified 10 critical defects, preventing potential miscalculations in risk models and ensuring 98% data accuracy for regulatory reporting.

Generated high-quality, labeled conversational datasets to optimize Alexa’s speech recognition and NLU models (sentiment analysis). Reduced response errors by 3%, enabling greater comprehension of diverse accents, dialects, and intents for seamless user interactions. Accelerated the development of 2+ new Alexa conversational features by collaborating with ML engineers to extract, organize, and label critical data, enabling informed feature selection. Elevated team performance by designing and delivering a training program for 15+ new hires. Increased data accuracy to 97%, earning a ’Rising Star’ and two ’Star Performer’ awards.