Rudrendu Paul

I am an Applied AI/ML and Marketing Measurement Science leader with 15+ years of building and scaling applied AI and machine learning products at Fortune 50 companies. I specialize in Causal Inference, Experimentation, Agentic AI systems, and enterprise AI strategy, applied to Retail Media Networks (RMN), Ads, Adtech, Martech, CPG, and E-Commerce. Published author with Springer Nature, Elsevier, ICML (Top 3 AI/ML Conference), and IEEE, and a regular contributor to leading AI/ML and analytics publications (views are my own)

San Francisco, USA •

10 posts •

#product experimentation

Product Experimentation: Stop Early Without P-Hacking Using mSPRT and Sequential Testing in Python

#product experimentation

Product Experimentation for LLM Platforms: Switchback Designs When User Randomization Breaks Market Equilibrium in Python

#ai agents

Product Experimentation with Regression Discontinuity: How an LLM Confidence Threshold Creates a Natural Experiment in Python

#product experimentation

Product Experimentation: Stop Early Without P-Hacking Using mSPRT and Sequential Testing in Python

Product Experimentation for LLM Platforms: Switchback Designs When User Randomization Breaks Market Equilibrium in Python

When Your Customer Is an AI Agent: How B2B Companies Stay Visible When Buyers Are AI Agents

Product Experimentation for Collaborative AI Features: Cluster Randomization for LLM-Based Tools in Python

Product Experimentation with Synthetic Control: Causal Inference for Global LLM Rollouts in Python

Product Experimentation with Regression Discontinuity: How an LLM Confidence Threshold Creates a Natural Experiment in Python

Product Experimentation with Propensity Scores: Causal Inference for LLM-Based Features in Python

Product Experimentation for AI Rollouts: Why A/B Testing Breaks and How Difference-in-Differences in Python Fixes It

The AI Governance Handbook: How to Build Responsible AI Systems That Actually Ship

How to Build and Secure a Personal AI Agent with OpenClaw