Machine Learning Scientist II
| Company | Wayfair |
|---|---|
| Location | Boston, MA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Mid Level, Senior |
Requirements
- Bachelor’s or advanced degree (Master’s, PhD) in Computer Science, Machine Learning, Mathematics, Statistics, or related field.
- 3-6 years of experience as an ML engineer, applied scientist, or research scientist, with a proven track record of delivering ML projects autonomously and driving measurable business impact, particularly in recommendations, search, or ranking.
- Expertise in recommendation systems, including candidate generation, ranking algorithms, and user-item modeling.
- Deep understanding of collaborative filtering, sequence modeling, and embedding-based personalization.
- Experience with end-to-end project ownership, including collaboration with business partners and strong written and verbal communication skills.
- Strong proficiency in Python and/or Java for building and deploying ML-driven recommendation systems.
- Experience deploying machine learning models in production environments, with a focus on cloud-based solutions such as GCP (BigQuery, GCS, Vertex AI, Composer), as well as workflow orchestration tools like Airflow, model tracking using MLflow, and containerization technologies like Docker.
Responsibilities
- Elevate recommendation quality: Design and implement scalable machine learning models to improve the ranking, and personalization of product recommendations across Wayfair.
- Develop robust retrieval systems: Build and optimize candidate generation pipelines that surface high-quality, personalized product recommendations at scale.
- Leverage user and product signals: Apply deep learning and representation learning to model user preferences, product attributes, and contextual signals for better recommendation performance.
- Innovate with cutting-edge techniques: Explore sequence modeling, embeddings, and multi-modal modeling to drive the next generation of recommender systems.
- Collaborate cross-functionally: Partner with product managers, engineers, and data scientists to align recommendation strategies with business objectives and user needs.
- Tackle recommendation-specific challenges: Solve key issues such as the cold-start problem, data sparsity, product compatibility and seasonality in dynamic environments.
- Advance the ML community at Wayfair: Contribute to internal knowledge sharing, author technical documentation, and represent Wayfair at top ML conferences like NeurIPS and RecSys.
Preferred Qualifications
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No preferred qualifications provided.