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, and internal campaign tools and products, ad formats and functionalities across all Yahoo brands including Yahoo Homepage, Yahoo Sports, Yahoo Finance, Yahoo News. This team is uniquely positioned to identify growth and revenue generation opportunities, design and implement solutions across consumer products and advertising platforms including video, display and native.
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.
Our Big Data footprints are among the largest few in the world, at double-digit petabyte scale. You will work with hundreds of billions of ad events monthly, building models that learn from delayed conversion signals, sparse reward data, and complex multi-touch attribution paths. The challenges span real-time prediction at sub-100ms latency, offline reinforcement learning from logged bandit feedback, causal inference for experimentation, and multi-objective optimization balancing publisher yield with advertiser outcomes.
If you are someone who thrives at the intersection of rigorous ML research and production-scale engineering, who gets excited about building learning systems that improve autonomously from real-world feedback, and who wants to shape the science strategy for a platform generating billions in advertising revenue, we want to hear from you!
Design and train production-grade ML models for conversion prediction, click-through rate estimation, engagement scoring, and advertiser ROAS forecasting at petabyte scale
Develop and deploy multi-task and multi-objective learning models that jointly optimize for publisher yield and advertiser outcomes
Build offline and online model training pipelines with automated retraining, model versioning, validation, and canary deployment workflows
Apply advanced ML techniques including gradient-boosted trees, deep neural networks, transformer architectures, and ensemble methods to advertising optimization problems
Design and build real-time and batch feature generation pipelines that capture user intent signals, contextual relevance, behavioral patterns, and advertiser performance indicators
Develop feature stores and feature serving infrastructure that provides low-latency access to hundreds of features at prediction time
Create novel features from cross-channel signals—search intent, content engagement, purchase behavior, and ad interaction history—to improve model accuracy
Establish feature importance analysis, drift detection, and automated feature quality monitoring
Design and lead the experimentation strategy for advertiser outcome optimization, including A/B tests, multi-armed bandit experiments, and interleaving designs
Build and maintain the statistical framework for experiment analysis—power calculations, significance testing, sequential analysis, and correction for multiple comparisons
Develop automated experiment monitoring, guardrail metrics, and early-stopping criteria to protect revenue while enabling rapid iteration
Translate experiment results into actionable insights for product, engineering, and business stakeholders
Design and build the closed-loop feedback architecture that connects ad delivery decisions to delayed conversion events, post-click engagement, and advertiser business outcomes
Develop reinforcement learning (RL) and contextual bandit systems for real-time bid optimization, dynamic floor pricing, and ad ranking that learn continuously from outcome feedback
Implement offline policy evaluation techniques (inverse propensity scoring, doubly robust estimation, replay methods) to safely evaluate new RL policies before online deployment
Design reward shaping and credit assignment mechanisms that handle delayed rewards, sparse conversion signals, and multi-touch attribution across the ad delivery lifecycle
Build autonomous learning systems where optimization agents self-improve from real-world feedback without manual intervention, with appropriate safety constraints and guardrails
Define the ML and data science strategy for closed-loop measurement and advertiser outcome optimization across the monetization platform
Mentor and provide technical guidance to data scientists, ML engineers, and data engineers across the team
Collaborate with product managers, engineering leads, sales, and advertiser-facing teams to define outcome metrics, success criteria, and measurement methodology
Publish findings, contribute to the broader ML community, and represent the team’s technical vision to leadership and external partners
Drive alignment across cross-functional stakeholders on the science roadmap, experiment priorities, and model deployment strategy
Ph.D. in Computer Science, Machine Learning, Statistics, Operations Research, or a related quantitative field with 8+ years of industry experience; or M.S. with 12+ years of relevant industry experience.
Demonstrated track record of shipping production ML systems that drive measurable business impact at scale.
8+ years of industry experience applying machine learning and statistical modeling to large-scale production systems
Deep expertise in supervised learning (classification, regression, ranking), including gradient-boosted trees (XGBoost, LightGBM), deep neural networks, and ensemble methods
Strong hands-on experience with reinforcement learning and/or contextual bandit algorithms (UCB, Thompson Sampling, policy gradient methods) applied to real-world optimization problems
Proven track record designing and analyzing large-scale A/B tests, including statistical rigor in power analysis, significance testing, and causal inference
Expert-level feature engineering skills—designing features from raw behavioral, transactional, and contextual data at petabyte scale
Production experience with ML frameworks: TensorFlow, PyTorch, JAX, Scikit-learn, XGBoost, or equivalent
Strong proficiency in Python and SQL; experience with distributed computing frameworks (Spark, Beam, Dataflow)
Experience with cloud ML platforms (Vertex AI, SageMaker, or equivalent) for model training, serving, and monitoring
Experience building end-to-end ML pipelines: data preparation, training, validation, deployment, monitoring, and retraining
Excellent communication skills with demonstrated ability to influence technical strategy and present to senior leadership
Track record of mentoring and elevating the capabilities of data science and ML engineering teams
Experience in ad tech, programmatic advertising, computational advertising, or publisher-side monetization (bidding optimization, auction design, yield management)
Experience with offline policy evaluation and counterfactual reasoning (inverse propensity scoring, doubly robust estimation)
Experience with multi-objective optimization, Pareto-optimal solutions, and constrained optimization in production settings
Experience with conversion modeling, attribution modeling, or marketing mix modeling
Experience with privacy-enhancing technologies, differential privacy, federated learning, or clean room analytics
Experience with real-time model serving at sub-100ms latency for high-throughput systems (500B+ events/month)
Experience with NLP, transformer models, or large language models applied to advertising or recommendation systems
Publications in top ML/AI venues (NeurIPS, ICML, KDD, WWW, RecSys, AAAI) or equivalent applied research contributions
This initiative aligns with our business goals and positions us for sustained success in the evolving digital advertising landscape. The closed-loop advertiser outcome capability is the intelligence layer that sits on top of our measurement data infrastructure and transforms raw event data into ML-driven optimization decisions.
It supports our strategic objectives to transition from impression-based monetization to outcome-driven advertising, establish industry leadership in AI-powered yield and outcome optimization, increase advertiser satisfaction and retention which in turn drives publisher revenue growth, and deliver the ML foundation for agentic AI advertising agents that optimize autonomously for both publisher and advertiser value. This investment directly supports our O&O publisher sites to increase their Revenue and profitability through smarter, outcome-aware monetization.
The material job duties and responsibilities of this role include those listed above as well as adhering to Yahoo policies; exercising sound judgment; working effectively, safely and inclusively with others; exhibiting trustworthiness and meeting expectations; and safeguarding business operations and brand integrity.
At Yahoo, we offer flexible hybrid work options that our employees love! While most roles don’t require regular office attendance, you may occasionally be asked to attend in-person events or team sessions. You’ll always get notice to make arrangements. Your recruiter will let you know if a specific job requires regular attendance at a Yahoo office or facility. If you have any questions about how this applies to the role, just ask the recruiter!
Yahoo is proud to be an equal opportunity workplace. All qualified applicants will receive consideration for employment without regard to, and will not be discriminated against based on age, race, gender, color, religion, national origin, sexual orientation, gender identity, veteran status, disability or any other protected category. Yahoo will consider for employment qualified applicants with criminal histories in a manner consistent with applicable law. Yahoo is dedicated to providing an accessible environment for all candidates during the application process and for employees during their employment. If you need accessibility assistance and/or a reasonable accommodation due to a disability, please submit a request via the Accommodation Request Form (www.yahooinc.com/careers/contact-us.html) or call +1.866.772.3182. Requests and calls received for non-disability related issues, such as following up on an application, will not receive a response.
We believe that a diverse and inclusive workplace strengthens Yahoo and deepens our relationships. When you support everyone to be their best selves, they spark discovery, innovation and creativity. Among other efforts, our 11 employee resource groups (ERGs) enhance a culture of belonging with programs, events and fellowship that help educate, support and create a workplace where all feel welcome.
The compensation for this position ranges from $160,965.00 - $349,885.00/yr and will vary depending on factors such as your location, skills and experience.The compensation package may also include incentive compensation opportunities in the form of discretionary annual bonus or commissions. Our comprehensive benefits include healthcare, a great 401k, backup childcare, education stipends and much (much) more.Currently work for Yahoo? Please apply on our internal career site.