: Data Science Manager, Storefront Recommendations

Data Science Manager, Storefront Recommendations

Boston, MA 

 

Wayfair Data Science powers automation & decision support across all Wayfair business units. Our algorithms tackle a varied & broad spectrum of challenges in the Wayfair marketplace; from empowering suppliers to easily add products to our catalog, to enabling our customers to discover and purchase a vast & diverse assortment of home goods. 

The Data Science Search & Recommendations team is looking for an experienced Data Scientist to focus on leveraging customer behavior and product attributes to personalize the Wayfair customer experience. In this role, youll partner with fellow data scientists, engineers, analysts, and product managers to bring our next generation deep learning ranking models to life. Youll spend one part of your day reading white papers and the other part of your day implementing them. Youll test and explore state-of-the-art deep learning architectures on terabytes of data. Youll explore how to evaluate and de-bias recommender systems offline. Youll scale solutions to serve billions of recommendations each day, explore the combinatorial explosion of product options in the Wayfair catalogue, and think about how to wrangle with real-time inference constraints. The models you develop will have tremendous business impact, directly measurable through our online A/B test platform. 

 

What Youll Do 

  • Own the full Data Science life-cycle from conception to prototyping, testing, deploying, and measuring its overall business value
  • Develop quantitative models, leveraging machine learning and advanced data analysis techniques
  • Architect and build technical platforms for our algorithmic engines to run at scale
  • Uncover deep insights hidden in our vast repository of raw data, and provide tactical guidance on how act on findings
  • Provide a strong partnership with business and engineering teams

 

What You'll Need

  • 4+ years of experience in a quantitative or technical work environment, and advanced degree (PhD) in a quantitative field (e.g. mathematics, economics, computer science, engineering, physics, neuroscience, operations research, etc.)
  • Intuitive sense of how quantitative and technical work aligns closely with business priorities and business value
  • Ability to effectively work with business leads: strong communication skills, ability to synthesize conclusions for non-experts and desire to influence business decisions
  • High comfort level with Python (preferred), or with other languages such as R, Java, C#, etc.,
  • Machine Learning experience (such as supervised/unsupervised learning, recommendation systems, reinforcement learning, deep learning, NLP, etc.)
  • Deep Learning, Learn-to-Rank, and Recommender systems experience strongly preferred
  • Ability to thrive in a dynamic environment where there can be degrees of ambiguity
  • Bonus points for intellectual curiosity and a strong desire to always be learning


About us

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.

Full-time