Machine Learning Manager, Advertising Science

About this role:

We are looking for an experienced ML Manager to lead a newly-formed team focused on building intelligent ML-based bidding systems (e.g. tROAS, dynamic bidding, smart campaigns) for Wayfair’s “Sponsored Products” advertising business. You will have the opportunity to develop 0-to-1 capabilities that unlock significant commercial value and contribute directly to Wayfair’s bottom line. You will partner closely with talented engineers and scientists to tackle some of Wayfair’s most intellectually challenging machine learning, latency, and scalability problems.

Wayfair's SMART (Search, Marketing, and Recommendations Technology) team is at the forefront of shaping how millions of customers discover and engage with products. We leverage cutting-edge machine learning, artificial intelligence, and data-driven strategies to deliver personalized shopping experiences. As a member of our SMART team, you'll be responsible for designing, implementing, and optimizing systems that enhance search capabilities, develop highly relevant product recommendations, and drive innovative marketing solutions.

What you’ll do:

• Lead the Intelligent Bidding pod within the Advertising Science Machine Learning group, responsible for building 0-to-1 intelligent bidding capabilities (e.g., target ROAS, dynamic bidding) for Wayfair’s advertising platform.
• Hire, develop, and coach a talented team of ML scientists to build scalable ML decision-making systems that directly contribute to Wayfair’s bottom line.
• Partner closely with cross-functional teams across engineering, science, analytics, and product to develop a science strategy and roadmap for Supplier Ads bidding systems.
• Design, build, deploy, and refine extensible, reusable, large-scale platforms that improve supplier experience, ROAS, and ad spend on Wayfair’s Sponsored Products.
• Build robust monitoring, alerting, and edge-case handling mechanisms.
• Collaborate with multiple science and engineering teams to drive the robust integration of our smart bidding platform with existing infrastructure and systems.
• Research new developments in advertising, sorting, recommendations, and open-source packages, incorporating them into our internal systems.

Who you are:

• 6+ years of experience (ideally 2+ years in a lead/manager role) building advanced machine learning models that solve real-world problems.
• Strong theoretical grasp of machine learning concepts combined with hands-on expertise in deploying web-scale ML-based decision-making systems into production.
• A hands-on, technical manager who can engage deeply with both core algorithm development and system design/architecture.
• Experience with data-driven opportunity sizing, prioritization, and a bias towards building iteratively and learning as you go.
• Comfortable making complex decisions (even in the face of ambiguity), making pragmatic trade-offs, and using goal-setting frameworks (e.g., OKRs).
• Proven track record of coaching and mentoring junior ML scientists and engineers — ranging from fresh PhD graduates to experienced ML scientists.
• Experience working with commercial stakeholders to translate business objectives into appropriately scoped ML models/systems, ensuring alignment between commercial and ML objectives.
• Strong written and verbal communication skills with the ability to influence senior stakeholders and steer strategy based on data-driven recommendations and analysis.
• Familiarity with ML model development frameworks, orchestration, and pipelines, with experience in Airflow, Kubeflow, or MLFlow, as well as Spark, Kubernetes, Docker, Python, and SQL.

Nice to have:

• Experience building intelligent bidding and/or advertising systems (e.g., CPC bidding, tROAS) for eCommerce or other two-sided marketplaces.
• Experience with online learning, reinforcement learning (RL), or ML-based control systems.