Machine Learning Scientist II
Wayfair is one of the world's largest online destinations for the home. Our Data Science and Machine Learning teams build the measurement systems that guide how we invest in marketing, improve customer experiences, and drive long-term value. We develop advanced tools and models that inform decisions across the entire company.

We are seeking a Machine Learning Scientist II to join the Measurement & Attribution team. This team leads the development of causal inference methodologies, attribution models, and experimentation frameworks that enable Wayfair to measure the true business impact of our marketing and product investments.

As an ML Scientist II, you'll design and validate new statistical and machine learning models for business questions such as estimating marketing return on investment (ROI), optimizing budget allocation, and improving experimentation sensitivity. You'll collaborate closely with engineers, product managers, marketers, and fellow data scientists to scale robust measurement systems across the organization.
What You'll Do

• Build and improve multi-touch attribution models using causal ML and statistical approaches (e.g., Shapley values, Double Machine Learning, Neural Networks)
• Research and apply quasi-experimental techniques (e.g., difference-in-differences, synthetic controls, instrumental variables) to quantify marketing effectiveness
• Develop internal Python packages and tooling to support attribution pipelines, causal model development, and test evaluation
• Scale modeling workflows using Google Cloud tools (e.g., BigQuery, Vertex AI, Cloud Functions, microservices)
• Partner with stakeholders in marketing, product, and operations to define success metrics and shape test designs
• Guide experimentation and lift study strategy for large marketing investments across channels like Paid Search, Social, Display, and TV

What You'll Need

• Ph.D. with 0-1+ years of experience, or 2+ years of industry experience with a Master's degree in a quantitative field (e.g., Statistics, Economics, Operations Research, Computer Science)
• Strong foundation in causal inference, experimental design, and statistical learning
• Fluency in Python for model development, data analysis, and package building (R a plus)
• Familiarity with modern data infrastructure (e.g., SQL, BigQuery, Airflow) and reproducible research workflows
• Strong written and verbal communication skills, with the ability to explain technical methods to non-technical stakeholders

Wayfair is fully committed to providing equal opportunities for all individuals, including individuals with disabilities. As part of this commitment, Wayfair will make reasonable accommodations to the known physical or mental limitations of qualified individuals with disabilities, unless doing so would impose an undue hardship on business operations. If you require a reasonable accommodation to participate in the job application or interview process, please let us know by completing our Accommodations for Applicants form.

Wayfair is one of the world's largest online destinations for the home. Whether you work in our global headquarters in Boston, or in our warehouses or offices throughout the world, we're reinventing the way people shop for their homes. Through our commitment to industry-leading technology and creative problem-solving, we are confident that Wayfair will be home to the most rewarding work of your career. If you're looking for rapid growth, constant learning, and dynamic challenges, then you'll find that amazing career opportunities are knocking.

No matter who you are, Wayfair is a place you can call home. We're a community of innovators, risk-takers, and trailblazers who celebrate our differences, and know that our unique perspectives make us stronger, smarter, and well-positioned for success. We value and rely on the collective voices of our employees, customers, community, and suppliers to help guide us as we build a better Wayfair and world for all. Every voice, every perspective matters. That's why we're proud to be an equal opportunity employer. We do not discriminate on the basis of race, color, ethnicity, ancestry, religion, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, genetic information, or any other legally protected characteristic.