Serve as the technical lead for AI-powered relevance, shopping, recommendation, and agentic commerce initiatives across Copilot, Shopping, and Ads experiences. Drive innovation in machine learning technologies including LLMs, SLMs, multimodal AI, retrieval, ranking, personalization, and recommendation systems. Define and execute the science roadmap for user intent understanding, product understanding, content relevance, and advertiser matching. Lead end-to-end ML development, including model architecture, training data strategy, evaluation, experimentation, calibration, and production deployment. Partner with engineering and product teams to deliver scalable, reliable, and cost-efficient AI systems. Shape the technical vision for future agent experiences, conversational shopping, and AI-assisted commerce scenarios. Drive measurable improvements in customer satisfaction, engagement, relevance quality, and business outcomes. Mentor scientists and engineers while raising the technical bar across machine learning, experimentation, and scientific rigor.

Required Qualifications:

Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Extensive experience building and shipping large-scale machine learning systems in search, recommendation, ranking, advertising, commerce, conversational AI, or personalization. Deep expertise in modern machine learning, including deep learning, transformers, representation learning, retrieval systems, recommendation systems, and foundation models. Demonstrated experience serving as a technical lead for large-scale cross-organizational initiatives. Proven ability to translate research innovations into production systems with measurable business impact. Experience with LLMs, SLMs, multimodal AI, and agentic systems. Experience in advertising, e-commerce, shopping, recommendation, or marketplace ecosystems. Experience developing AI-powered assistants, commerce experiences, or personalization platforms. Experience optimizing distributed training and inference systems on large GPU clusters. Experience mentoring principal-level engineers, scientists, and technical leaders.