Senior Applied Data Scientist, Search Ranking

What you'll do

• Develop and deploy scalable ML models for search ranking, browse personalization, and semantic retrieval.
• Design and execute offline and online experiments to improve relevance, engagement, and conversion metrics.
• Build scalable feature pipelines, evaluation frameworks, and automated ML workflows for production systems.
• Implement advanced techniques including embeddings, transformers, vector search, and GenAI/RAG systems.
• Improve query understanding and long-tail search relevance across large retail catalogs.
• Balance model quality with strict requirements for latency, scalability, and reliability in high-traffic environments.
• Partner with Product, Engineering, and Infrastructure teams to align technical solutions with business goals.
• Mentor junior scientists and contribute to best practices in ML engineering and operational excellence.

What we're looking for

• PhD or MS in Computer Science, Statistics, Applied Mathematics, Physics, or a related quantitative discipline.
• 3+ years of industry experience in Machine Learning, Data Science, Search, NLP, Personalization, or related ML systems.
• Exceptional understanding of retrieval/ranking systems, semantic search, NLP, vector search, or related ML domains.
• Strong coding skills in Python and SQL with experience in distributed data processing ecosystems.
• Demonstrated experience building and deploying end-to-end production ML systems.
• Understanding of production ML system tradeoffs including latency, scalability, reliability, and operational excellence.
• Experience with modern ML approaches such as embeddings, transformers, semantic retrieval, RAG systems, or GenAI technologies.
• Experience designing experiments and interpreting online/offline metrics.