Senior Scientist - Machine Learning

As a Senior ML Scientist at Wayfair, you'll shape the future of how millions of customers are engaged through intelligent, timely, and personalized communication powered by scalable AI systems.

Who We AreWayfair is moving the world so that anyone can live in a home they love - a journey enabled by more than 3,000 Wayfair engineers and a data-centric culture. The Customer Technology (CT) science team builds and owns all the Machine Learning (ML) products that power all search, marketing, and recommendations technologies across Wayfair. Our algorithms tackle a broad spectrum of challenges in the Wayfair marketplace; from empowering suppliers to easily add products to our catalog, to enabling our customers to discover and purchase a vast & diverse assortment of home goods, to driving marketing experiences customers love all at web scale.

Within this, the Notifications Science team focuses on building intelligent systems that optimize how and when we engage with customers across channels such as Email, Push, and SMS. These systems balance short-term engagement with long-term customer value, enabling personalized, relevant, and timely communication at scale.We are looking for an experienced ML scientist to lead the research and development of ML systems that power Wayfair's customer engagement and notification platforms. This includes send decisioning, cross-channel optimization, and personalized content selection.

In this role, you will be responsible for building the next generation of Wayfair's ML stack for notifications, including systems that determine which customers to contact, when to contact them, through which channel, and with what content. You will collaborate closely with engineering, analytics, and product partners within CT to define technical direction and deliver scalable, high-impact solutions. This role is a key contributor within the CT Science organization, with visibility across marketing, product, and platform teams.

What You'll DoDrive the development of ML systems for notification send decisioning, including ranking and personalization componentsBuild and improve systems that optimize cross-channel customer engagement (Email, Push, SMS), balancing revenue, engagement, and customer fatigueDevelop multi-objective optimization frameworks to drive long-term customer value while minimizing unsubscribe and disengagement riskContinually improve the team's ability to build efficient and scalable ML models by leveraging industry best practices and maintaining high standards of model quality and robustnessCollaborate closely with product and engineering partners to build and evolve Wayfair's next-generation notification and customer engagement stackDesign and iterate on decisioning systems under competing objectives using experimentation and data-driven optimizationContribute as a senior individual contributor within the CT Science group and the broader ML community at WayfairMaintain a strong customer-centric perspective in how problems are framed and solved

What You'll NeedBachelor's or Master's degree in Computer Science, Mathematics, Statistics or a related field.10+ years of experience in building large-scale ML algorithms4+ years of demonstrated experience owning and delivering end-to-end ML systems or providing technical leadership on complex ML problemsStrong theoretical understanding and practical experience applying statistical models (e.g., regression, clustering) and ML algorithms (e.g., decision trees, neural networks, transformers, NLP techniques) at scale.Experience working on ranking, recommendation, ads, or notification systemsProficiency in programming languages such as Python or R, and ML libraries such as TensorFlow or PyTorch, to develop production-grade systems.Ability to partner cross-functionally to own and shape technical roadmapsIntellectual curiosity and a strong desire to continuously learn!

Nice to havePhD in Computer Science, Mathematics, Statistics or related fieldExperience with marketing technology, customer engagement platforms, or send optimization systemsFamiliarity with multi-objective optimization, causal inference, or experimentation frameworksFamiliarity with Generative AI and its applications in personalization or content generationExperience with cloud platforms (GCP, AWS, Azure) and ML orchestration tools (Airflow, Kubeflow, MLflow)