Malay Nilabh

Background

About Malay Nilabh

I am an AGM - Data Scientist at John Deere, where I use my 12+ years of experience in machine learning, artificial intelligence, advanced analytics, data science, and business intelligence to deliver data-driven solutions for various business problems. I have a strong background in machine learning, forecasting, optimization, and data visualization, as well as credentials in The Data Scientist's Toolbox and R Programming from Coursera. At John Deere, I developed a product based on generative ai models to create a chatbot that can democratise data for end user to take important business decision. Prior to Deere I was with Amazon as a Data Scientist where I worked on developing products based on ensemble tree-based machine learning models to forecast the volume and size distribution of packages for middle mile stations. I also used Python, Redshift, and Quicksight to create dashboards and reports for the stakeholders. Previously, I worked as a lead data scientist at Gramener, where I designed and implemented applications for price optimization, marketing spend optimization, and appointment scheduling, using techniques such as decision tree regression, linear regression, and pulp. I also led and supported teams to perform survey analysis, account segmentation, active learning, and topic modeling for clients in technology, insurance, and other sectors. I am passionate about leveraging data and analytics to generate insights, improve decision making, and create value for customers and businesses.