Learn How to Run and Assess Experiments with AI
Hosted by Sravya Madipalli, Hai Guan, and Shane Butler
Wed, Mar 4, 2026
8:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course



Wed, Mar 4, 2026
8:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course



What you'll learn
Running experiments with a clear, decision-focused framework
How to run experiments using a clear, decision-led framework
Identify gaps and risks before acting on experiments with AI
Join AI Builders Slack Community. Link: bit.ly/ai-connect
Why this topic matters
You'll learn from
Sravya Madipalli
Senior DS Leader, Ex-Microsoft
Sravya Madipalli is a Senior Manager of Data Science with 14+ years of experience helping teams make better decisions with data. She has built and led data science and product analytics teams at Microsoft, eBay, Nextdoor, and Superhuman (prev. Grammarly), working closely with product, engineering, marketing, and leadership. Her expertise spans experimentation, metrics design, modeling, analytics, and translating complex user behavior into clear, actionable insights.
Hai Guan
Head of Data at Ontra, Ex-LinkedIn
Hai Guan leads the data organization at Ontra, the leading legal tech AI solutions for private markets. He previously led Data Science & Analytics at LinkedIn, Nextdoor, Pinterest, and Meta. He's spent a decade teaching product development teams how to ask questions that actually drive decisions—and now teaches how to combine that judgment with AI to move 10x faster.
Shane Butler
Principal Data Scientist at Ontra
Shane Butler is a Principal Data Scientist at Ontra, where he leads evaluation strategy for AI product development in the legal tech domain. He has more than ten years of experience in product data science and causal inference, with prior roles at Stripe, Nextdoor, and PwC. Shane is also the co-host of the AI podcast Data Neighbor, where he interviews product, data, and engineering leaders who are pioneering the next generation of data science and analytics in an AI-driven landscape.
