Who We Are | Machine Learning Science at Wayfair

We work closely with stakeholders across the business to build scalable ML solutions and algorithmic platforms that drive incremental revenue, enhance the customer experience, & improve customer loyalty. With 10+ expansive workstreams (Search, Pricing, Personalization & Recommendations, Merchandising, Marketing, Measurement, B2B, Sales, Services, and Supply Chain ), and more than 20 specialized sub-teams, the projects that our teams work on directly impact our customers on a massive scale. Take for example (1) Developing novel machine learning models to identify latent customer preferences so that the best products can be highlighted in real-time to our customers, (2) Optimizing profitability and business strategy by accurately forecasting future demand, costs and margin; Estimating the long-term impacts on customer behavior of short-term actions via experimentation, or (3) Creating machine learning solutions to detect product duplicates across the millions of products in our catalog and scaling the solution to handle real-time uploads of new products by partners.

Data is at the heart of everything we do and there is very little at Wayfair that our Machine Learning team does not touch. They work closely with various teams across the business to build and scale novel solutions to business problems via machine learning. 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 and the algorithms you create will directly impact the customer’s experience.

What You’ll Do | Responsibilities

• Develop and scale state-of-the-art machine learning methods to address core business problems
• Architect and support technical platforms for our algorithmic engines to run at scale
• Build end-to-end ML solutions and pipelines to run in real-time, scaling algorithmic insights to impact millions of Wayfair customers
• Own the full machine learning life-cycle from conception to prototyping, testing, deploying, and measuring overall business value driven by your work as part of a dynamic team
• Identify new opportunities and insights from the data (where can the models be improved? what is the projected ROI of a proposed modification?)
• Collaborate with data scientists to create maintainable, scalable and debuggable code by bringing strong software development practices
• Work with a team of friendly and motivated scientists and engineers to build and scale novel solutions to business problems

What You’ll Need | Qualifications

• Graduating from a PhD program, or 3+ years of relevant experience with a MSc/BaSc in quantitative field (engineering, computer science, economics, etc.)
• Proficiency in Python or one other high-level programming language 
• A strong theoretical understanding and solid hands-on expertise deploying reinforcement learning solutions into production.
• Strong written and verbal communication skills, ability to synthesize conclusions for non-experts, and overall bias towards simplicity 
• Intellectual curiosity and enthusiasm about continuous learning
• Well-rounded understanding of machine learning fundamentals (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

Nice to have

• Experience with Python ML ecosystem (numpy, pandas, sklearn, XGBoost, etc.) and/or Apache Spark Ecosystem (Spark SQL, MLlib/Spark ML)
• Familiarity with GCP (or AWS, Azure), ML model development frameworks, ML orchestration tools (Airflow, Kubeflow or MLFlow)
• Experience with Spark, Kubernetes, Docker are nice to have 

No matter the position that you choose, you will have the opportunity to play a critical role in a growing company while also operating with a high level of executive visibility. The team is focused on creating strategic solutions that steer customer behavior, influence key decision making and quantify our impact within the e-commerce space. Our diverse and fun employees enjoy an environment of strong ownership and quick feedback from building, experimenting and iterating on high-impact work.

An Important Note about Wayfair’s In-Office Policy:

All interns, co-ops, and corporate employees will be in office in a hybrid capacity. Employees in the technology org will work in the office on designated days, Tuesday, Wednesday, and Thursday, and work remotely the other 2 days of the week.

At this time, Wayfair does not provide sponsorship for employment authorization for this position.