Overview
On Site
Full Time
Skills
Science
Media
AIM
Microsoft
Use cases
Pinterest
Roadmaps
Mentorship
Knowledge sharing
Research
IMPACT
Digital marketing
Management
Optimization
Collaboration
Communication
SQL
Software engineering
Python
Machine Learning (ML)
FOCUS
Cloud computing
Google Cloud
Google Cloud Platform
GCS
Vertex
Artificial intelligence
Workflow
Orchestration
Docker
Computer science
Economics
Mathematics
Estimating
Statistics
Testing
A/B testing
Marketing
ADS
Advertising
Leadership
Creative problem solving
SAP BASIS
Data
Privacy
Data security
Job Details
Who we are
The Bidding and Decisioning team under Wayfair Marketing Science is responsible for developing and managing machine learning models and strategies to optimize marketing decisions across paid media channels. We aim to create a leading, customer-focused, ML-powered decision engine that uses extensive first- and third-party data, in collaboration with engineering teams, to optimize every aspect of our marketing. This includes spend management, content optimization, segmentation, and campaign structures across major digital ad platforms like Google, Meta, Pinterest, and Microsoft Bing, etc. Our work impacts hundreds of millions in ad spend, ensuring we provide the right marketing experience for each customer, at the right time, and at the right price throughout their buying journey.
What you'll do
Who you are
Nice to have
About Wayfair Inc.
Wayfair is one of the world's largest online destinations for the home. Whether you work in our global headquarters in Boston or Berlin, 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.
Your personal data is processed in accordance with our Candidate Privacy Notice ( If you have any questions or wish to exercise your rights under applicable privacy and data protection laws, please contact us at
The Bidding and Decisioning team under Wayfair Marketing Science is responsible for developing and managing machine learning models and strategies to optimize marketing decisions across paid media channels. We aim to create a leading, customer-focused, ML-powered decision engine that uses extensive first- and third-party data, in collaboration with engineering teams, to optimize every aspect of our marketing. This includes spend management, content optimization, segmentation, and campaign structures across major digital ad platforms like Google, Meta, Pinterest, and Microsoft Bing, etc. Our work impacts hundreds of millions in ad spend, ensuring we provide the right marketing experience for each customer, at the right time, and at the right price throughout their buying journey.
What you'll do
- Identify new opportunities and evolve the spend management optimization framework, expanding its coverage for new use cases.
- Build and implement ML solutions to enhance efficiency, defining problem statements, and delivering against technical and research objectives in a timely manner.
- Collaborate cross-functionally with Marketing, MarTech (Product & Engineering), and ML Platform teams to align roadmaps, adopt best practices, and build scalable, sustainable bidding solutions.
- Engage with external vendors, including Google, Meta, Pinterest, to understand technical capabilities, inform bidding solution decisions, and influence strategic roadmaps.
- Collaborate closely with engineering, infrastructure, and ML platform teams to adopt best practices for building and deploying scalable ML services.
- Foster the development of junior ML scientists on the team through mentorship, knowledge-sharing, and support for subject matter expertise initiatives such as code reviews.
Who you are
- Bachelor's or advanced degree (Master's, PhD) in Computer Science, Economics, Mathematics, Statistics, or related field.
- 3-6 years of experience as an ML engineer, applied scientist, or research scientist, with a proven track record of delivering ML projects autonomously and driving measurable business impact, particularly in digital marketing decisioning (e.g., spend management, content optimization, segmentation) and/or ML decisioning systems.
- Experience with end-to-end project ownership, including collaboration with business partners and strong written and verbal communication skills.
- Strong SQL skills and excellent software engineering skills in Python.
- Experience deploying machine learning models in production environments, with a focus on cloud-based solutions such as Google Cloud Platform (BigQuery, GCS, Vertex AI, Composer), as well as workflow orchestration tools like Airflow, model tracking using MLflow, and containerization technologies like Docker.
Nice to have
- PhD in Computer Science, Economics, Mathematics, Statistics, or related field.
- Expertise in causal inference, multi-armed bandits (MAB), reinforcement learning, control systems, and heterogeneous treatment effect estimation
- Strong background in statistical analysis, hypothesis testing, A/B testing methodologies, and quasi-experimental measurement methods.
- Experience with specific marketing platforms: familiarity with Google Ads, Meta Ads, or other major advertising platforms is a definite advantage.
About Wayfair Inc.
Wayfair is one of the world's largest online destinations for the home. Whether you work in our global headquarters in Boston or Berlin, 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.
Your personal data is processed in accordance with our Candidate Privacy Notice ( If you have any questions or wish to exercise your rights under applicable privacy and data protection laws, please contact us at
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.