Lead AI Scientist Ad Monetization & Measurement

It takes powerful technology to connect our brands and partners with an audience of hundreds of millions of people. Whether you're looking to write mobile app code, engineer the servers behind our massive ad tech stacks, or develop algorithms to help us process trillions of data points a day, what you do here will have a huge impact on our business-and the world.

A Little About Us


We are an industry-leading direct-to-consumer and ad tech solution for advertisers and publishers. Our innovative ad tech gives one-stop access to Yahoo, Inc.'s trusted data, high-quality inventory and demand, creative ad experiences, and industry-leading machine learning at global scale. The Consumer Monetization team's charter is to Find, Evaluate, Build, and Scale new monetization products and internal campaign tools across all Yahoo brands.

We are currently transitioning our monetization engine to an AI-first framework, where autonomous agents optimize for long-term user value and advertiser outcomes in real-time.

A Lot About You


As a Sr. Principal Data Scientist on the Consumer Monetization Platform Engineering team, you will be the foundational technical leader defining the functional strategy for how AI-augmented intelligence closes the loop between ad serving and advertiser business outcomes. You will architect the ML models, reinforcement learning systems, and feedback architectures that transform our platform into an autonomous learning ecosystem. You thrive at the intersection of rigorous research and production-scale engineering.

You are a leader who views AI not just as a product feature, but as a core tool to augment your own research and engineering lifecycle-leveraging emerging tools to accelerate the path from hypothesis to production. You get excited about building systems that self-improve and want to shape the science strategy for a platform generating billions in revenue.


Responsibilities



Model Training & ML Strategy

• Define the strategic technical roadmap for production-grade ML models, leveraging AI-augmented prototyping tools (e.g., Sage Maker Jumpstart) to accelerate the deployment of transformer architectures and multi-task models.
• Architect multi-objective learning models that jointly optimize for publisher yield and advertiser outcomes, implementing AI-driven validation frameworks to ensure model safety and robustness.
• Establish organization-wide standards for model training pipelines, automated retraining, and AI-augmented model versioning.

• Oversee the design of real-time and batch feature generation pipelines, integrating AI-driven diagnostics (e.g., Vertex AI) to monitor feature health, drift detection, and automated quality monitoring at petabyte scale.
• Implement AI-assisted tools to automate feature discovery from cross-channel signals-search intent, content engagement, and purchase behavior-to improve model predictive power.

• Lead the organization's experimentation strategy, utilizing AI-driven simulation environments to evaluate multi-armed bandit and causal inference designs before online deployment.
• Develop AI-powered guardrail monitoring systems that autonomously adjust experiment parameters to protect revenue while maximizing learning velocity.

• Architect the next-generation closed-loop feedback architecture, where RL-based agents self-improve through automated reward shaping and credit assignment mechanisms.
• Lead the implementation of AI-augmented offline policy evaluation (OPE) techniques to safely validate reinforcement learning policies in highly sparse and delayed reward environments.

• Contribute to the development of the organization's broader functional strategy, ensuring monetization science aligns with Yahoo's long-term business objectives.
• Establish governance frameworks using tools like Weights & Biases to standardize ML experiment tracking and ensure reproducibility across multiple squads.
• Mentor and provide technical guidance to senior and principal data scientists, elevating the technical bar for the entire monetization engineering organization.

• Ph.D. in Computer Science, Machine Learning, Statistics, or a related field with 8+ years of industry experience; or M.S. with 12+ years of relevant industry experience.
• Proven experience leveraging AI productivity tools and LLMs to accelerate complex research workflows and code generation.
• Proficiency in prompt engineering and structured interaction with AI models to optimize large-scale system diagnostics and research documentation.
• Deep expertise in supervised learning, gradient-boosted trees, and reinforcement learning (RL/Bandits) applied to organizational-scale problems.
• Strong proficiency in Python and SQL; experience with distributed computing (Spark) and cloud ML platforms (Vertex AI, Sage Maker) for production-grade deployment.
• Expertise in exercising judgment when to deploy AI-augmented solutions versus…