Thoughts & Learnings
Writing about analytics, experimentation, and lessons from building data-driven products.
I'm working on writing up my learnings from building ML models, running experiments, and finding cost leakage in operational data. Check back soon!
The architecture, training data choices, and surprising lessons from A/B testing a recommendation engine at scale.
What makes an experiment actually useful? Hypothesis quality, metric selection, and when to ship vs when to kill.
How I audited the entire payout pipeline and found three separate cost leakage vectors that were invisible to the finance team.
The flat table architecture and internal tools that cut our team's analysis overhead by 40%.
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