Data Scientist
Current• Effective Communication of Data Science Solutions: Developed numerous ad-hoc machine learning models to address a variety of business problems across Sales, Supply Chain, Retail, Finance and Marketing, providing quick and actionable insights. Regularly communicated complex technical findings to non-technical senior leaders and VP-level executives through detailed and engaging presentations, influencing high-level strategic decisions.• End-to-End Machine Learning Pipeline: Designed and deployed a machine learning framework to identify and prioritize prospective customers for Grainger’s KeepStock business segment involving developing custom SQL queries for data extraction and transformation, orchestrating workflows with Airflow, containerizing applications with Docker, implementing predictive models with Sklearn, publishing and updating model output to Snowflake tables for seamless data accessibility and reporting, and monitoring with Grafana and AWS for performance evaluation.• NLP in Measuring Seller Effectiveness: Developed and implemented an NLP pipeline for topic modeling and keyword extraction for seller CRM notes using NLTK and LDA. Integrated these insights into Random Forest models along with other seller KPIs to understand seller effectiveness, resulting in improved model accuracy and business decision-making.• A/B Testing and Profitability Measurements: Driving cross-functional collaboration with Supply Chain and Retails conducting experiments like retail space optimization, supply chain delivery capabilities. Used A/B testing to measure ROI and sales lifts by performing statistical hypothesis testing and create recommendations to high level executives.• Full Cycle Data Science Consultancy for Sales Territory Redesign: Developed a Logistic Regression models to identify and prioritize prospective customers for effective seller territory redesign. Collaborated with finance and coverage teams to roll out the project nationwide.
