Suhas Ranganath

About Suhas Ranganath

I’m an Applied Scientist and AI Lead who builds and deploys large-scale machine learning systems that deliver real business value. At Microsoft, I work in a horizontal AI team focused on enhancing Microsoft commerical business through state-of-the-art solutions in Agentic AI, time series forecasting, document classification. I lead initiatives that incorporate cutting-edge techniques like large language models (LLMs), retrieval-augmented generation (RAG), temporal neural networks, transformers, and graph neural networks. These solutions have directly contributed to saving millions of dollars in cloud consumption and improving customer satisfaction, and they’ve resulted in multiple patents and publications in the Microsoft Journal of Applied Research.Prior to this, I was part of Walmart Labs’ search and ads team, where I led the design and production deployment of machine learning algorithms that significantly improved search relevance and drove over $X00 million in business revenue. I developed and launched systems for hierarchical query classification, query localization, and catalog-driven knowledge graphs, using techniques like hierarchical CNNs, transformers, DeepWalk, and GNNs. I also built scalable microservices for query understanding and played a central role in Walmart’s academic collaborations, co-authoring papers published at SIGIR, WSDM, and ACM WebConf.My journey began in research, where my Ph.D. work focused on building AI-driven models to understand large-scale behavior in social networks. I studied how users seek information, engage with campaigns, and interact with marketers on platforms like Twitter, and developed algorithms to identify influencers, predict protest participation, and understand rhetorical communication. My research was published in leading venues like TKDE, and TIST.In every role, I’ve combined a deep technical foundation with a strong product sense and a collaborative mindset. I enjoy mentoring early-career scientists, partnering with engineering and business teams, and translating research into reliable production systems. Whether I’m building time-sensitive early warning systems, fine-tuning LLMs for document tagging, or driving innovation in search relevance, my goal is to make AI impactful, explainable, and scalable.

Experience

1. Senior Applied Scientist

   Amazon

   Mar 2026 — Present · Bengaluru, IN

   Building AI for Amazon Payment Products

Education

• Arizona State University

  Doctor of Philosophy - PhD

• Arizona State University

  Master of Science - MS

Skills

• Statistics
• Political Campaigns
• Programming
• Databases
• Algorithms
• Embedded Systems
• R
• Machine Learning
• Data Analysis
• Signal Processing
• Android
• Simulink
• Vhdl
• Simulations
• Mongodb
• Information Seeking
• Microsoft Excel
• Java
• Deep Learning
• Microsoft Office
• Matlab
• Data Mining
• Social Search
• Research
• Sql
• Linux
• Social Media
• Verilog
• Pspice
• Nosql
• Python
• Latex
• Algorithm Design
• Algorithm Analysis
• Labview