As a Senior Principal Data Scientist on the Consumer Monetization Platform Engineering team, you will be the technical leader defining how machine learning and AI are applied to close the loop between ad serving and advertiser business outcomes. You will design and build the ML models, reinforcement learning systems, feature generation pipelines, experimentation frameworks, and feedback architectures that transform our platform from impression-based optimization to outcome-driven intelligence.

Model Training & ML AlgorithmsDesign and train production-grade ML models for conversion prediction, click-through rate estimation, engagement scoring, and advertiser ROAS forecasting at petabyte scaleDevelop and deploy multi-task and multi-objective learning models that jointly optimize for publisher yield and advertiser outcomesBuild offline and online model training pipelines with automated retraining, model versioning, validation, and canary deployment workflowsApply advanced ML techniques including gradient-boosted trees, deep neural networks, transformer architectures, and ensemble methods to advertising optimization problems

Feature Generation & DataDesign and build real-time and batch feature generation pipelines that capture user intent signals, contextual relevance, behavioral patterns, and advertiser performance indicatorsDevelop feature stores and feature serving infrastructure that provides low-latency access to hundreds of features at prediction timeCreate novel features from cross-channel signalssearch intent, content engagement, purchase behavior, and ad interaction historyto improve model accuracyEstablish feature importance analysis, drift detection, and automated feature quality monitoring

AB Testing & ExperimentationDesign and lead the experimentation strategy for advertiser outcome optimization, including AB tests, multi-armed bandit experiments, and interleaving designsBuild and maintain the statistical framework for experiment analysispower calculations, significance testing, sequential analysis, and correction for multiple comparisonsDevelop automated experiment monitoring, guardrail metrics, and early-stopping criteria to protect revenue while enabling rapid iterationTranslate experiment results into actionable insights for product, engineering, and business stakeholders

Feedback Loops & Reinforcement LearningDesign and build the closed-loop feedback architecture that connects ad delivery decisions to delayed conversion events, post-click engagement, and advertiser business outcomesDevelop reinforcement learning (RL) and contextual bandit systems for real-time bid optimization, dynamic floor pricing, and ad ranking that learn continuously from outcome feedbackImplement offline policy evaluation techniques (inverse propensity scoring, doubly robust estimation, replay methods) to safely evaluate new RL policies before online deploymentDesign reward shaping and credit assignment mechanisms that handle delayed rewards, sparse conversion signals, and multi-touch attribution across the ad delivery lifecycleBuild autonomous learning systems where optimization agents self-improve from real-world feedback without manual intervention, with appropriate safety constraints and guardrails

Leadership & StrategyDefine the ML and data science strategy for closed-loop measurement and advertiser outcome optimization across the monetization platformMentor and provide technical guidance to data scientists, ML engineers, and data engineers across the teamCollaborate with product managers, engineering leads, sales, and advertiser-facing teams to define outcome metrics, success criteria, and measurement methodologyPublish findings, contribute to the broader ML community, and represent the teams technical vision to leadership and external partnersDrive alignment across cross-functional stakeholders on the science roadmap, experiment priorities, and model deployment strategy