Product Manager, Personalization and Fraud @ Wayfair Inc.

An Important Note about Wayfair's In-Office Policy and Salary: Please note that this is a hybrid role based in Boston, and will require you to work in the office on Tuesdays, Wednesdays, and Thursdays. The salary range for this position is $126,500 - $137,500 per year. The base salary offered may vary depending on location, job-related knowledge, skills, and experience. Restricted stock units will be provided as part of the compensation package. Who We Are: At Wayfair, we believe everyone should live in a home they love—and we’re committed to making that possible through cutting-edge technology. Our Financing & Loyalty (FnL) Platforms team powers the backend services and machine learning capabilities that enable seamless, personalized, and secure commerce experiences for our millions of customers. We are seeking a Product Manager to drive platform capabilities across Personalization and Fraud Detection, two high-impact domains that ensure customers see the most relevant offers while enjoying trusted and secure transactions. You’ll work closely with engineers, data + ML scientists, and business stakeholders to deliver resilient, data-driven systems that scale with Wayfair’s global business. What You’ll Do: Own the Platform Vision & Roadmap: Define and drive product strategy for personalization and fraud detection platform capabilities—powering everything from user-level content tailoring to real-time risk evaluation. Design Scalable Data Systems: Partner with Engineering and Data Science to evolve core data pipelines, data models, and service architectures that support rules + ML-driven decisioning in real-time environments. Collaborate Across Functions: Align with business partners (e.g., Fraud Ops, Loyalty/Personalization teams, Data Engineering, Finance) to translate goals into technical requirements and end-to-end solutions. Leverage ML Principles Thoughtfully: Help operationalize model development pipelines, support inference and scoring services at scale, and prioritize features that make ML applications robust and extensible. Drive Data Stewardship: Ensure data quality, system observability, and auditability across all…

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