Sr Applied Scientist, Core Shopping Data Science

About the job

A customer may encounter a recommendation that is irrelevant, two widgets offering similar products, and confusing labels. Customers notice all of it. The ranking systems that chose those impressions largely do not, because until recently there was no way to turn a judgment about how a page feels into a signal a model could learn from.

LLMs changed that. We can now take an ambiguous statement about customer perception, make it a judgment that holds up consistently at scale, and validate it against what shoppers actually do next. What we cannot do is call a large model in a ranking request path at large scale. Every shopping session passes through those systems under latency and cost budgets that leave no room for one. So we can describe quality far better than we can optimize for it, and closing that gap is what this role exists to do.

You will distill LLM quality judgments into models compact enough to serve online, model how customers respond to the defects they encounter so we know which ones are worth trading engagement to prevent, and work with Search, Homepage and Detail Page ranking teams to get those signals into online objectives. Scientists on this team own the measurement side of the problem. You own the half that turns their judgments into systems that act and test them in controlled experiments.