Lead Data Scientist

Who We Are

Wayfair’s Advertising business is rapidly expanding, adding hundreds of millions of dollars in

profits to Wayfair. We are building Sponsored Products, Display & Video Ad offerings that cater

to a variety of Advertiser goals while showing highly relevant and engaging Ads to millions of

customers. We are evolving our Ads Platform to empower advertisers across all sophistication

levels to grow their business on Wayfair at a strong, positive ROI and are leveraging state of the

art Machine Learning techniques.

The Advertising Optimization & Automation Science team is central to this effort. We

leverage machine learning and generative AI to streamline campaign workflows, delivering

impactful recommendations on budget allocation, target Return on Ad Spend (tROAS), and SKU

selection. Additionally, we are developing intelligent systems for creative optimization and exploring agentic frameworks to further simplify and enhance advertiser interactions.

We are looking for seasoned ML Scientists to join the Advertising Optimization & Automation Science team. In this role, you will be responsible for the development of budget, tROAS and SKU recommendations and other machine learning capabilities supporting our ads business. You will work closely with other scientists, as well as members of our internal Product and Engineering teams, to apply your engineering and machine learning skills to solve some of our most impactful and intellectually challenging problems to directly impact Wayfair’s revenue.

What you’ll do

• Provide technical leadership in the development of an automated and intelligent advertising system by advancing the state-of-the-art in machine learning techniques to support recommendations for Ads campaigns and other optimizations.
• Design, build, deploy and refine extensible, reusable, large-scale, and real-world platforms that optimize our ads experience.
• Work cross-functionally with commercial stakeholders to understand business problems or opportunities and develop appropriately scoped machine learning solutions
• Collaborate closely with various engineering, infrastructure, and machine learning platform teams to ensure adoption of best-practices in how we build and deploy scalable machine learning services
• Identify new opportunities and insights from the data (where can the models be improved? What is the projected ROI of a proposed modification?)
• Research new developments in advertising, sort and recommendations research and open-source packages, and incorporate them into our internal packages and systems.
• Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on.

What you’ll need

Nice to have