Machine Learning Scientist II (Search)

We are looking for Machine-Learning Scientists for research and development of the ML systems that power our real-timeSearch Typeahead, Search Guidance, Query Understanding and Interpretation, Query Refinement and Recommendations, and emerging GenAI-powered search capabilities. 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 revenue.
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.
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.
Requirements:
Bachelor's degree with 4+ years of experience or master's degree with 2+ years of experience in computer science, mathematics, statistics, or a related field.
Strong theoretical understanding of statistical models such as regression, clustering, and ML algorithms such as decision trees, neural networks, etc.
Solid understanding of algorithms for search retrieval, ranking, relevance, query understanding, NLP, or recommender systems.
Good proficiency in the Python ML ecosystem (pandas, NumPy, scikit-learn, XGBoost, etc. ).
Solid hands-on expertise deploying machine learning solutions into production.
Strong written and verbal communication skills.
Intellectual curiosity and enthusiasm about continuous learning.
Nice to have:
Experience in e-commerce or online search systems.
Knowledge of modern NLP techniques (transformers, embeddings, semantic search).
Experience applying generative AI/LLMs (e. g., GPT, LLaMA) to real-world problems such as search, personalization, or content generation.
Experience with deep learning frameworks like PyTorch.
Familiarity with vector databases and information retrieval frameworks (e. g., Elasticsearch, Vespa).
Familiarity with GCP (or AWS, Azure), ML model development frameworks, and ML orchestration tools (Airflow, Kubeflow, or MLFlow).
Experience with the Apache Spark ecosystem (Spark SQL, MLlib/Spark ML).

Experience
3-6 yrs