Principal Applied Scientist

Our Signals Modeling team builds the intelligence that powers how the advertising marketplace understands user behavior, measures impact and optimizes outcomes from initial impressions through downstream conversions and long-term advertiser value. We develop large-scale learning systems that infer intent and causal effects from incomplete and noisy feedback, enabling principled decision-making across ranking, bidding, pricing, and budget allocation. Our work sits at the foundation of marketplace optimization, where accurate attribution and measurement directly influence billions in advertising spend. The team designs and operates state-of-the-art modeling platforms spanning representation learning, weak-supervision, multi-objective training, calibration, and rigorous experimentation. We transform sparse engagement signals into reliable learning targets and build models that remain robust under delayed conversions, selection bias, and rapidly shifting marketplace dynamics. As a Principal Applied Scientist, you will help define the future of data-driven attribution and causal measurement, shaping the methodologies that determine how value is estimated and optimized across the ecosystem. You will partner across research, engineering, and product leadership to introduce advanced inference techniques into production systems operating at massive scale. This is a high-ownership role focused on solving structurally hard problems where ground truth is limited, experimentation is non-trivial, and scientific rigor is essential to unlocking durable marketplace advantage.