Wayfair is looking to solve real-world problems for customers by building and leveraging cutting edge Machine Learning and AI products
Requirements
- Proficiency in Python or one other high-level programming language
- Strong theoretical understanding and solid hands-on expertise deploying machine learning solutions into production
- 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
Responsibilities
- 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 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
- Build robust monitoring, alerting, edge-case handling mechanism
- 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
Other
- Minimum 0-1 years of experience with PhD, or 3+ years of industry experience with MSc/BaSc in STEM -- engineering, computer science, economics, etc.
- Strong written and verbal communication skills, ability to synthesize conclusions for non-experts, and overall bias towards simplicity
- Intellectual curiosity and enthusiastic about continuous learning
- Must be based, or plan to relocate, to Boston or Seattle, and will be expected to comply with their team's hybrid work schedule requirements
- Must have a degree in a relevant field (e.g. engineering, computer science, economics, etc.)
