INTERVIEW
summary
Flipkart describes concrete systems: a selection optimizer that decides how many SKUs to carry (phones, water bottles), a price elasticity optimization engine and a price‑value scoring model that predicts choice between two ₹3,000 shoes, and ensemble models plus on‑ground interventions that cut return rates. Production use includes reinforcement learning and deep nets for customer engagement, supply‑chain and seller‑fraud detectors, tooling in R, Python and PySpark, and measurable P&L impact.
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