Machine Learning Data Scientist

Wayfair’s Data Science team builds the algorithmic systems that drive our business. With 7 expansive workstreams (Pricing, Personalization & Recommendations, Merchandising, Marketing, B2B, Computer Vision, and Operations), and more than 20 specialized subteams, our projects span from enormously broad to minutely specific. The projects that our teams work on are driven from the ground up – we look for entrepreneurial individuals that want to take ownership over their own agenda and thrive in a collaborative team environment. Take for example (1) determining how to implement AR technology to improve the shopping experience versus (2) optimizing diversity in the sales we highlight in a customer’s email. There is very little at Wayfair that our Data Science team does not touch. They work closely with stakeholders across the business to ensure that our data driven insights do not collect dust. With an in-house A/B testing platform and rolling code deployments, our team can quickly and clearly see the impact that its work has on the company at large.

What You'll 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
  • Leverage our work in order to increase adoption across our business partners, to drive real business value
  • Uncover deep insight hidden in our vast repository of raw data, and provide tactical guidance on how act on findings
  • Use data to improve the decision-making of our employees, and ultimately, to enhance the experience of our customers and our suppliers

What You'll Need

  • 2+ years of experience in a quantitative or technical work environment or advanced degree (PhD) in 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 programming, e.g. languages such as Python, R, Scala, Java, C++, C#, PHP, etc
  • Machine Learning experience (such as supervised/unsupervised learning, recommendation systems, reinforcement learning, deep learning, etc.)
  • Ability to thrive in a dynamic environment where there can be degrees of ambiguity
  • Bonus points for being hands on, while providing technical leadership and driving strategic initiatives for your team


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