B2B Recommendation Engine - Spearheaded a team of 4 (Data Analysts) on a retailer recommendation engine for a major FMCG client using Azure Databricks and Azure ML Services, achieving a 15% increase in recommendation accuracy and a 12% improvement in must-sell precision. (Neural Collaborative Filtering, FP Max)- Established A/B testing frameworks to measure the impact of recommendation strategies on engagement and sales resulting in a 20% increase in user engagement and a 10% rise in sales conversion. - Led cross-functional teams in iterative development, integrating technical and business perspectives. - Designed a proof of concept (PoC) for a B2B recommendation engine, which was successfully pitched and acquired by different client, leading to its ongoing development and deployment on AWS. Analytical Engine Migration- Led a team of 5 (Data Analysts and Data Engineer) on sales reporting enhancement by migrating from SAP Business Object to Power Apps, decommissioning legacy systems and addressing inefficiencies. - Mapped a comprehensive data model with 21 views over multiple tables and data from SAP BW, enabling robust data integration and analysis. - Created 80+ measures for a self-service cockpit, enhancing the client's ability to perform data analysis. - Ensured data security and integrity through strong data governance. - Facilitated Change Management for over 200 legacy users, transitioning 800+ reports to the new platform.

As a part of Global Supply Chain Analytics team, we are tasked to developed Analytical and Business Intelligence Products / Solutions / Tools. As a part of core development team member, I have contributed immensely in complete SDLC process using Agile methodology. Some basic responsibilities include Development of Framework, DB Stored Procedures development, Data extraction, cleaning and preparation, EDA, BI Dashboard Development etc.- Created from scratch, an end-to-end Python, SQL and Linux based Framework for Demand Forecasting used for batch production planning.- Used Multi-Layered/Iterative approach with complete Machine Learning Pipelines.- Developed Inventory Optimization (Push – Pull) System.- Developed Distribution Center Locator (using Latitude and Longitude for sales density).- Created Delivery route optimization framework.- Created Business Intelligence dashboards using Tableau and QlikSense.- Developed GUI for orchestration of above developed tools using DASH.- Automated SCM Alert system, utilized FPDF module in Python to generate Alert Report PDF's for parts in production when triggered using KPI's like IHTR, ITOR, ISPR and Shortage Ratio- Used Programing Tools like Python, Dash, Tableau, QlikSense, Oracle DB 19c, R etc.

Recruitment Analytics - Enhanced Random Forest model accuracy by ~3% through hyperparameter tuning. - Migrated models to Databricks and transitioned code to production.- Developed data pipelines for diverse data sources, conducted PoC for enhancements, presented findings and collaborated with analysts.