Senior Applied Scientist

Drive modeling and data innovations for ad interaction outcome prediction under partial and noisy feedback. Focus on building estimated conversion models, designing data-driven attribution and weak-label generation pipelines, and developing robust learning and calibration methods for scenarios where true user outcomes are sparse, delayed, or unobservable. Design and evaluate multi-task and proxy-signal models, improve offline and online measurement frameworks, and translate modeling advances into production-ready systems that directly impact ad ranking, bidding, advertiser ROI, and user experience at web scale. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Experience presenting at conferences or other events in the outside research/industry community as an invited speaker. Experience working with noisy, weak, or proxy labels, including building training signals from indirect user behavior. Experience with conversion, outcome, or funnel modeling (e.g., post-click modeling, engagement modeling, attribution, or similar problems). Experience with large-scale online marketplaces or ads/recommendation systems. Experience designing or operating multi-task / auxiliary-task learning systems. Experience with offline evaluation and online A/B experimentation for ML systems. Ability to independently drive modeling projects from problem definition through production and iteration.