Idan Richman Goshen
Idan Richman Goshen is a data-driven technologist with an M.A. in Economics and more than a decade of experience turning raw data into business impact. Before leading the Data Science team at Lusha, he built production-grade machine-learning systems at Localize and Dell.
Session
At 1:23 a.m. on 26 April 1986, the RBMK-4 graphite-moderated reactor at Chernobyl exploded. Every dosimeter still working inside flat-lined at 3.6 R/h, its maximum reading, while lethal radiation raged unseen. That single detail from Chernobyl is the perfect allegory for what can go wrong in modern machine-learning pipelines: clipped features, hidden distribution shifts, missing logs, runaway feedback loops, and more. This talk unpacks key incidents from the disaster and map each one to an equivalent failure mode in production ML, showing how silent risk creeps into data systems and how to engineer for resilience. Attendees will leave with a practical set of questions to ask, signals to track, and cultural habits that keep models (and the businesses that rely on them) well clear of their own meltdowns. No nuclear physics required.