Anudeep Ramisetty

Enhancing Google Gemini in input file based Question Answering - Comparative Analysis, Response Curation, Planning and Auto Code Execution with Feedback on Analytical Queries.

• Customer Segmentation - Redefined existing RFM to multi-layered RFM that helped Marketing and Finance teams to align and allocate budget optimally for targeted promotions
• Customer Lifetime Value Prediction - Developed a Regression-Tree based model to predict CLV of customers & Customer Early Value Prediction based on RFM segmentation using the first few weeks of activity
• Product Recommendation system - Successfully built a recommendation engine that recommends different sports betting products to players using history of betting behavior with 3 different models - ALS and hybrid models
• Promo Optimization - Developed Customer Acquisition Strategy by Optimizing Promotional Campaigns for new customers that maximizes the cohort's Revenue and Retention
• Providing Business Intelligence insights and reports on various analyses that contribute to ARR (Revenue).

• CPG & Retail Analytics - Portfolio Optimization for a 500 crore+ CPG MNC
• SKU Lift - Rationalization & Assortment Planning - USA
• Advanced Analytics - Support & Optimization for various analytical tools designed for clients such as Customer Analytics, Marketing Analytics, Spends & ROI optimization planners etc - UK and Russia Markets
• Marketing sales attribution and spends optimization for multiple retailers with product level modelling - US Petcare Market

• Drug Launch Sequence Optimization - EU Zone. Generate Launch Sequence in EU markets by optimizing NPV
• Analyzed pricing, promotion, production cost and other factors like At Launch, Post Launch and IRP rules to calculate final effective pricing.

• Developing a cutting edge platform for hygiene tracking, employee tracking and face-mask detection in real time video feed in a human-bot environment
• Extensively researched on similarity/background subtraction, transfer learning and R-CNN for the application
• In parallel, building a full stack product pipeline to monitor and track SOPS for the industrial kitchen involving guided bot-human interactions in food production, using Django, CSS, JQuery, Bootstrap and AJAX.

• Time Series Forecasting - Developed a Multi-Model Forecast Engine with demonstrably well off models (Exponential Smoothing, ARIMA, LSTM), which aids in multiple scenarios of forecasting for hundreds of products involved in supply chain
• Multi Label Multi Class Text Classification with Sentiment Analysis - Extensively researched on unsupervised text classification and opinion mining for multiple aspects of multiple classes (food reviews) and developed a decent Aspect Based Sentiment Analysis model prototype, inspired by NLP Architect from Intel
• Menu Engineering - Analyzed sales data and uncovered meaningful KPIs, derived popular items, cuisines etc
• Built a model that generates best possible combos of items in the menu
• Collectively they are best used to design unique menus with higher conversion rates
• Recommendation System - A vanilla Collaborative Filtering model with Euclidean distance as measure
• Co-Worked on architectural framework for the AI brain of the company to monitor, track and control the entire ML workflow.

• Worked with 6 Entrepreneurs from launching to full-scale operational brands
• Built tools for clients to automate tasks such as Pricing, Brand Performance Reports, Location Intelligence and Competitive Intelligence using Flask
• Built relationships and partnered with vendors in providing raw materials at cheaper prices, which resulted 18% decrease in client operational expenses
• Monitored sales performance and supported clients with ad-hoc business models and specific marketing plans, which resulted in 60% increase in sales.