Who We Are

Which products, out of millions of options, are customers seeing when they browse our websites? How to dynamically improve product rankings to account for changes in customer behavior?

Wayfair’s Recommendations team provides the core platforms and services that allow our customers to discover and buy the products they love by serving the right content to every customer on every touchpoint. Our systems leverage Wayfair’s extensive customer and product data to deliver trusted and valuable recommendations and results in real-time using custom machine learning models. Our mission-critical services are called over a billion times a day. Recommendations is a core competency for Wayfair and a high priority lever we’re using to drive outsized growth, customer value and profitability.

The Product Recommendations team is searching for an individual to join our Default Recommendations sub-team. This team develops the algorithms that power our overall product recommendations, accounting for ~90% of products shown to customers across various placements. An example can be found on our tech blog. In this role, you’ll partner with fellow data scientists, engineers, analysts and product managers to apply data science and machine learning skills to bring our next generation recommendation platform to life. The ideal candidate enjoys sitting at the boundary of data science and engineering, and is comfortable working with partners across the organization to distill ambiguous business asks into technical plans. This is an exciting opportunity to join a growing team with a tangible impact on the performance of Wayfair overall. You will directly impact and drive forward key business initiatives. Above all, you’ll get to work on problems that are both intellectually challenging and drive real, measurable impact through our online A/B test platform. 

Candidates for this position may be based in Boston, MA or Mountain View, CA and will be expected to comply with their team’s hybrid work schedule requirements.

What You’ll Do 

• Develop quantitative models, leveraging machine learning and advanced data analysis techniques to improve our product recommendations.
• Own the full Data Science/Machine Learning life-cycle from conception to prototyping, testing, deploying, and measuring its overall business value.
• Coordinate, prepare, launch and assess live experiments in order to measure the incremental impact of your own work and/or the work of partner teams.
• Uncover deep insights hidden in our vast repository of raw data, and provide tactical guidance on how act on findings.
• Drive adoption and utilization of your products across the organization in ways that drive real business value.
• Architect and help define the required technical platforms that enable us to produce models at scale.

What You’ll Need

• 2+ years experience with advanced programming languages and big data/distributed system tools like Python, R, Scala, Java, Hive, SQL, Spark, etc.
• 2+ years of professional machine learning experience (such as supervised/unsupervised learning, recommendation systems, reinforcement learning, deep learning, NLP, etc.), and familiarity with recommender systems.
• Familiarity with ML model development frameworks, ML orchestration and pipelines with experience in either Airflow, Kubeflow or MLFlow as well as Spark, Python, and SQL.
• Ability to effectively work in a dynamic environment, where there can be degrees of ambiguity, with high-level business stakeholders: strong communication skills, ability to synthesize conclusions for non-experts and a desire to influence business decisions.
• Excellent organizational, analytical, and hypothesis-driven critical thinking skills to identify business opportunities and transform data into actionable insights.
• Familiarity with Machine Learning platforms offered by Google Cloud and how to implement them on a large scale (e.g. BigQuery, GCS, Dataproc, AI Notebooks).
• MS or PhD degree in a quantitative or related field (e.g. mathematics, economics, computer science, engineering, physics, neuroscience, operations research, etc.) or equivalent work experience.