Design, implement, and evaluate machine learning models for recommendation, ranking, and personalization
Conduct applied research to improve model quality, robustness, and efficiency
Develop and execute experimentation strategies using offline evaluation and online testing
Work with complex datasets to understand user behavior and system performance
Collaborate with engineering and product teams to transition research into production systems
Share research results in clear, accessible ways with both technical and non-technical audiences
Stay informed about advances in recommendation systems and related ML methodologies

Advanced degree (PhD or MS) in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience
Experience developing or applying recommendation, ranking, or personalization models
Strong foundation in machine learning, statistics, and data analysis
Proficiency in Python and modern ML frameworks (e.g., PyTorch)
Ability to conduct independent research and collaborate effectively in team environments
Prior publications or submissions to RecSys or closely related venues
Experience working with large-scale data and computational systems
Experience with representation learning, sequence modeling, or large-scale optimization
Experience transitioning research prototypes into production systems

Research Scientist, Recommendation Systems

Key skills