Yash Kothari

Most companies have data. But they don't have systems that turn that data into decisions that actually scale. You're probably sitting on analytics tools that should be driving growth - but instead, they're generating reports nobody acts on. Inventory systems that forecast the wrong products. ML models that sounded great in the pilot but break in production. I get it. I spent 5+ years solving exactly these problems.Here's what I've learned: The difference between analytics that looks impressive and analytics that drives revenue comes down to one thing - building systems that work when nobody's watching.My approach: • Diagnose data pipelines to find where quality breaks down • Build ML/GenAI systems with production-grade reliability (not just demos) • Partner with engineering and data science to deploy at scale • Deliver measurable outcomes: cost reduction, cycle time improvements, forecast accuracy Technical stack: Python, SQL, LangChain, RAG, AI/ML, Tableau, AWS, Fivetran, DBT, Airflow