This is a senior IC leadership position for someone with 10+ years of prior industry experience in a research role, with significant experience in Marketing Attribution. As a Principal Scientist within our Data Science and Machine Learning group, you will drive progress on our biggest challenges, turning business requests and creative ideas that do not have an obvious blueprint into scalable modeling solutions that run in real-time. This is an individual-contributor role focused on research and implementation of novel approaches and techniques that cut across our entire stack: Attribution modeling systems at Wayfair drive management of well over $1B annual marketing spend across a rapidly growing international footprint, directly informing business strategy & driving algorithmic management of both digital and offline marketing. By leveraging your depth of experience to diversify & improve our approaches to marketing valuation, you will touch the experience of all of our customers across our sites, apps, and marketing channels, at a recognized leading company in the performance marketing space. You will get an opportunity to work with one of the largest e-commerce datasets out there, come up with innovative algorithms and solutions, work with a growing team of ~150 to test these solutions in live experiments, and implement them as scalable production systems.
What you will do:
* Solve novel marketing attribution problems, building out production systems for valuation of returns on marketing investment, relying on your knowledge of machine-learning, optimization & economic modeling
* Keep abreast of relevant research on marketing attribution, conversion attribution, marketing performance measurement, marketing/media mix modeling, and approaches to privacy-robust attribution -- and use it to come up with your own ideas of how to improve & diversify our current methods
* Work with cross-functional teams of engineers, scientists, and analysts, to devise implementation and experimentation strategies
* Be a key contributor to the data science and machine-learning community, both within and outside of Wayfair
What you bring:
* 10+ years of experience in research & development, with substantial time spent both in an academic setting and within industry or a research-lab type of environment
* History of coming up with innovative and creative quantitative solutions, demonstrated through practical business impact and/or publications
* Previous expertise in marketing attribution, including both MMM & multi-touch-attribution approaches, as well as with experimental measurement of marketing returns & validation of attribution results
* You are hands on and comfortable coding on an industry-standard machine-learning stack (Hadoop/Bigquery, Spark, Python, Jupyter)
* You love seeing your work through to customer impact!
About Wayfair Inc.
Wayfair is one of the worlds 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, were 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 youre looking for rapid growth, constant learning, and dynamic challenges, then youll find that amazing career opportunities are knocking.
No matter who you are, Wayfair is a place you can call home. Were 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. Thats why were 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, or genetic information.