Design and implement machine learning models for search and recommendation systems, including ranking, retrieval, personalization, and query understanding
Build ranking and recommendation models using user behavior, embeddings, content signals, and contextual features
Develop personalization systems that tailor results based on user behavior, preferences, and contextual signals
Collaborate with data and search engineers to build scalable data pipelines supporting search and recommendation systems
Partner with software engineers to integrate ML models into production services via APIs
Design and execute A/B tests to evaluate model performance and business impact
Monitor offline and online metrics to identify opportunities for improving relevance, ranking, and engagement
Apply modern ML and GenAI techniques to improve search and discovery experiences
Contribute to best practices in modeling, experimentation, and production ML systems

5+ years of experience in data science, machine learning, or applied AI
Strong experience with search, recommendation systems, or ranking problems
Hands-on experience with Elasticsearch and/or Solr
Strong Python skills and experience with ML frameworks such as TensorFlow or PyTorch
Experience with large-scale data processing tools such as Spark and distributed systems
Experience integrating ML models into production systems via APIs
Experience with experimentation frameworks and A/B testing
Strong understanding of ML fundamentals: supervised learning, ranking models, embeddings, deep learning
Exposure to GenAI tools (OpenAI APIs, LangChain, or similar) is a plus
Strong communication skills and ability to work cross-functionally with engineering and product teams
Experience working in Agile environments
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Physics, or related quantitative field preferred

Senior Data Scientist, Search & Recommendations

Key skills