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# Machine Learning Scientist II - Pricing & Economics

- **Company:** Wayfair LLC
- **Location:** Boston, MA, United States
- **Experience:** Starter
- **Salary:** $176,000.0 - $184,250.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, Apache Hive, Python (Programming Language), Machine Learning, NumPy, Azure Machine Learning, Workflow Management Systems, Apache Spark, Pandas, Build Management, Scikit Learn, Kubernetes, Information Technology, Xgboost, Machine Learning Operations, Docker
- **Published:** August 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d29aa5cb1cd369bc

## About the Role

* Minimum 0-1 years of experience with PhD, or 3+ years of industry experience with MSc/BaSc in STEM - engineering, computer science, economics, etc.
 * Proficiency in Python or one other high-level programming language
 * Must have a strong theoretical understanding and solid hands-on expertise deploying machine 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 enthusiastic about continuous learning

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

## Description

* 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


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