Are you passionate about solving complex classification challenges at massive scale? We are seeking a Data Science Manager to join our ASIN Classification team within WAVE (World Wide AI Enablement). In this high-impact role, you will lead a team of scientists and engineers to architect, deploy, and operationalize advanced machine learning models that drive ASIN classification across a range of compliance programs. From supervised and unsupervised ML approaches to Large Language Models and Small Language Models, you will guide your team in harnessing state-of-the-art techniques to deliver accurate, scalable classification solutions for millions of ASINs. Key job responsibilities • Lead and manage a team of Applied Scientists, Data Scientists, and Engineers, fostering a culture of innovation, scientific rigor, and operational excellence • Define the team's science roadmap and prioritize classification initiatives across compliance programs • Own end-to-end delivery of classification solutions from problem framing and data strategy through model deployment and production monitoring • Drive architecture decisions including model selection, feature engineering from product catalogs, and evaluation metric frameworks • Translate ambiguous, large-scale compliance challenges into well-scoped data science and ML workstreams • Collaborate with business teams to convert business requirements into scalable ML solutions • Establish and monitor classification metrics, model performance KPIs, and production health dashboards to ensure continuous improvement • Partner cross-functionally with Science, Engineering, Product, and Operations teams to align science investments with business priorities • Build scalable data environments and ML pipelines to support model training, evaluation, shadow testing, and production inference at scale • Mentor and develop team members through career coaching, technical guidance, and structured growth plans • Communicate complex technical concepts effectively to non-technical stakeholders and senior leadership • Drive operational rigor ensuring zero-disruption deployments, data quality standards, and robust experimentation practices • Stay current with latest research, publications, and application of techniques to production systems