Data Scientist with Learn to Rank Experience

Role: Data Scientist with Learn to Rank Experience
Location: 100% Remote
Duration: 12+ Months Contract (High Extension Possibility)
Start Date: Immediate / ASAP Client Domain: Retail

What They are Looking For:

We are seeking a highly skilled Data Scientist with a strong background in e-commerce search technologies to join our team on an immediate basis. The ideal candidate will have experience working with Learn to Rank (LTR) models, vector search, and retrieval algorithms to drive relevance and performance in large-scale search systems.

Required Skills & Experience:

• Proven experience in e-commerce or similar domains focused on search and ranking.
• Strong expertise in Learn to Rank, vector similarity search, and retrieval models.
• Python and libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience with search engines (e.g., Elasticsearch, Solr, Vespa) and ANN frameworks (e.g., Faiss, Annoy, Milvus).
• Solid understanding of information retrieval concepts, relevance metrics, and evaluation methods.
• Familiarity with large-scale data processing (e.g., Spark, Hadoop) is a plus.

Key Responsibilities:

• Design, develop, and implement search ranking models using Learn to Rank approaches.
• Apply vector search techniques to improve relevance and personalization in search results.
• Develop scalable data pipelines to support retrieval algorithms and feature engineering.
• Collaborate with engineering, product, and UX teams to improve search experience.
• Analyze large-scale behavioral and clickstream data to inform algorithmic decisions.
• Rapidly prototype and iterate on models with measurable business impact.

This role will focus heavily on improving large-scale search relevance, personalization, and retrieval performance for an enterprise e-commerce platform.