Machine Learning Scientist, Wayfair

• Bachelor's or Master’s degree in Computer Science, Mathematics, Statistics, or related field
• 5-7 years of industry experience in developing machine learning algorithms and deploying them into production at web scale
• Proficiency in Python or one other high-level programming language
• Familiarity with productionized code bases and PRs; comfort mentoring junior scientists to improve their code quality
• Concrete hands-on expertise deploying machine learning solutions into production
• Theoretical understanding of statistical models such as regression, clustering and ML algorithms such as decision trees, neural networks, etc
• Deep domain knowledge of at least one of: NLP & Natural Language Understanding, Deep Learning & Transformer architectures (including frameworks such as PyTorch, Tensorflow, etc.), or Search & Information Retrieval
• Strong written and verbal communication skills, ability to synthesize conclusions for non-experts, and overall bias towards simplicity
• Intellectual curiosity and enthusiasm for continuous learning

Desirable

• The Search ML team is responsible for the algorithms that power Wayfair's global search experience across all countries and stores as well as all other recommendation services at Wayfair (item-to-item, browse, email, etc.) for both our core e-shopping experience and brand-based content
• In support of that mission, we are looking for machine-learning scientists with deep domain expertise and practical experience in at least one of the following: NLP & Natural Language Understanding, Deep Learning & Transformer architectures, or Search & Information Retrieval
• In this role, you’ll partner with fellow scientists, engineers, analysts, and product managers to apply science and machine learning skills to directly impact Wayfair’s customers, revenue, and profitability
• Design, build, deploy and refine large-scale machine learning models and algorithmic decision-making systems that solve real-world problems for customers
• Work cross-functionally with commercial stakeholders across product and analytics to understand business problems or opportunities and develop appropriately scoped analytical solutions
• Collaborate closely with various engineering, infrastructure, and ML platform teams to ensure adoption of best-practices in how we build and deploy scalable ML services
• Identify new opportunities and insights from the data (where can the models be improved? what is the projected ROI of a proposed modification?)
• Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on
• Foster the development of junior ML scientists on the team through mentorship and knowledge-sharing