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# Machine Learning Scientist II - Marketing & Measurement Science

- **Company:** Wayfair LLC
- **Location:** Boston, MA, United States
- **Experience:** Starter
- **Salary:** $176,000.0 - $184,250.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Data Analysis, Artificial Neural Networks, BigQuery, Data Infrastructure, Python (Programming Language), Machine Learning, SQL Databases, Google Cloud, Information Technology, Microservices
- **Published:** August 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ea5dca8689c63280

## About the Role

* 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

## Description

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


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