Introduction

Developed and deployed an end-to-end dashboard in Amazon QuickSight, providing real-time visibility into inbound network volumes and flow, enabling stakeholders to make data-driven decisions by viewing information at various granularities.

• Used CHAID analysis on 20,000 ASINs to identify key drivers for cross-docking decisions. The model, developed with a 70/30 test-control split, achieved 77% accuracy, 2e-16 statistical significance, and 0.8026 sensitivity, demonstrating strong performance in detecting positive outcome

• Spearheaded the Low Average Seller Price (ASP) Cost-Saving project, resulting in $356 million in financial savings through strategic cost optimization and financial modeling.

• Streamlined data pipelines and automated data flows from multiple sources, reducing manual reporting efforts by 40% and increasing the efficiency of decision-making processes.

• Collaborated with cross-functional teams to ensure the accuracy and relevance of key performance indicators (KPIs), improving operational transparency and resource allocation.

• Proactively identified data trends and anomalies, leading to a 15% improvement in inbound volume tracking accuracy and enhancing overall logistics efficiency.

• Generated end to end ETL and ELT process using Python and SQL for Data ingestion and transformation in Google cloud platform

• Used BigQuery ML to create and execute demand forecasting models in BigQuery using standard SQL queries

• Developed scripts using Visual Basics, Power Query and Excel to Automate UAT testing, leading to reduction in time taken for data validation from a day to less than 30 minutes.

• Created Sales dashboard in Tableau that provide a comprehensive overview of the business to assist higher management in decision making process.

· Developed data pipelines (ETL) using python (pandas) script to extract and transform CSV data and store into Microsoft SQL Server.

· Designed and built the Database Schema in MS SQL Server Management Studio for data ingestion

· Created interactive dashboard to provide a single glass pane view to analyze top to down performance of the builder business by applying advanced excel tools (Power pivot, OLAP cubes) and macros.

· Developed standardized procedures and predictive algorithms to estimate the value of opportunities and forecast sales using statistical analysis.  

· Created email alert system using visual basic which informed sales team about opportunities that are maturing soon.

 Project lead for customer database creation using Python:

• Web scraped potential client company’s contact information for targeted marketing from public sources using Selenium (Python)

• Extracted and cleaned client level data using advanced excel tools and techniques

• Enhanced the contact database from 800 contacts to 31000

• Co-developed interactive dashboard using VBA and advanced filters to easily traverse through the database as per the requirements

• Provide analytic insight and recommendations to market clients based on the database.

• Tracked data and developed reports on weekly basis to assess campaign performance

Consultant, Product Quality Complaints Automation for Fresenius Medical Care:

• Developed model using Python for complaint detection, identification and plausible solutions for multiple Fresenius products

• Worked onsite as a client engagement and solutions consultant

• Displayed strong verbal, written skills as well as develop client-ready presentations

• Co-led the offsite technical team to deliver client requirements swiftly and accurately