Develop and deploy scalable ML models for search ranking, browse personalization, semantic retrieval, and query understanding systems
Design and execute offline and online experiments to improve relevance, engagement, conversion, and customer satisfaction
Build scalable feature pipelines, evaluation frameworks, and ML workflows for production systems
Apply modern ML techniques including embeddings, retrieval/ranking models, transformers, NLP, vector search, and GenAI/RAG systems
Improve query understanding, catalog understanding, semantic retrieval, and long-tail search relevance across large retail catalogs
Partner closely with Product, Engineering, and Infrastructure teams to align technical solutions with business priorities and operational requirements
Drive data-informed decision making through deep analysis, experimentation, and business impact measurement
Balance model quality with latency, scalability, reliability, and infrastructure efficiency in large-scale production systems
Contribute to best practices in experimentation, ML engineering, and operational excellence
Mentor junior scientists and collaborate across teams to improve Search and Browse experiences

PhD or MS in Computer Science, Statistics, Applied Mathematics, Physics or related quantitative discipline
3+ years of industry experience in Machine Learning, Data Science, Search, NLP, Personalization or related ML systems
Exceptional experience with 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 hands-on experience building and deploying production ML systems at scale
Strong 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
Strong problem-solving skills with the ability to independently drive projects in moderately ambiguous environments
Very good communication and cross-functional collaboration skills
Constant learner mentality who stays current with new and evolving AI technologies via formal training and self-directed education

Senior Applied Data Scientist – Search and Browse, Applied ML, NLP, LLMs

Key skills