Design Product Analysis with Claude Code

Hosted by Shane Butler, Hai Guan, and Sravya Madipalli

Wed, Apr 8, 2026

7:00 PM UTC (1 hour)

Virtual (Zoom)

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AI Analytics for Builders
Shane Butler, Sravya Madipalli, and Hai Guan
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What you'll learn

Turn vague requests into decision-grade briefs

Use Claude Code to formalize the decision, success metrics, guardrails, and “what changes our mind” thresholds.

Build a full analysis design doc in Claude Code

Generate a structured 1-pager: scope, cohorts, hypotheses, methods, outputs, plus risks and mitigations.

Validate the plan in Claude Code before execution

Surface confounders, selection effects, baseline gaps, and scope creep. Add checks that reduce risk of failure.

Join AI Builders Slack Community. Link: bit.ly/ai-connect

Learn Agentic Analytics workflows and AI Eval design. Get feedback on what you’re building from other AI builders.

Why this topic matters

Analytics velocity is limited by ambiguity and rework, not compute. Claude Code lets you draft analysis designs fast, but speed only helps if the plan is decision-grade and defensible. This lesson teaches a standardized workflow for creating and pressure-testing analysis designs inside Claude Code, so you can move from idea to trusted decision faster, with fewer false conclusions.

You'll learn from

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.

Hai Guan

Head of Data at Ontra

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.

Sravya Madipalli

Senior Manager, Data Science @Superhuman (Prev. Grammarly)| 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.

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