Rudrendu Paul

@rudrendupaul

San Francisco, USAJoined April 2026

About

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)

Available for

Technical writing, conference speaking, advisory roles, and AI strategy consulting.

Rudrendu Paul's blogs

freeCodeCamp Englishfreecodecamp.org/news10 posts

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Recently published

RPRudrendu Paulfreecodecamp.org1d ago · 21 min read

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

Your AI product experiment reaches statistical significance on day 14 of a planned 30-day run, measuring a causal inference question: did the LLM-based feature genuinely improve outcomes? Every produc

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RPRudrendu Paulfreecodecamp.org3d ago · 28 min read

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

Your team ships an intelligent query-routing feature for an LLM SaaS platform. The feature reads each incoming request in real time and decides whether to send it to the fast standard model or the mor

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RPRudrendu Paulfreecodecamp.orgMay 28 · 12 min read

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

In April 2026, the 2X AI Innovation Lab published the inaugural AI Visibility Index, analyzing how 70 B2B companies appear across the generative AI environments that buyers now use to research and sho

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RPRudrendu Paulfreecodecamp.orgMay 22 · 22 min read

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

Every product experimentation team running causal inference on LLM-based collaborative features eventually hits the same wall: your users aren't independent. Your team ships an AI meeting summarizer t

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RPRudrendu Paulfreecodecamp.orgMay 12 · 21 min read

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

Every product experimentation team doing causal inference on LLM-based features eventually hits the same wall: when the provider ships a new model version, there's no holdout. Your infrastructure team

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Rudrendu Paul

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Rudrendu Paul's blogs

Recently published

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

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Rudrendu Paul

About

Available for

Rudrendu Paul's blogs

Recently published

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