Sr Applied Scientist, Core Shopping Data Science
Core
Distill LLM quality judgments into compact online models and quantify customer responses to shopping defects to improve ranking objectives.
Role type
Senior Applied Scientist (Core Shopping Data Science)
Builds
Compact models for Search, Homepage, and Detail Page ranking; defect impact analysis systems; quality-aware objective formulations.
Domain
E-commerce / Machine Learning / Large Language Models
Deliverable
production ML models
Required skills
Machine learning model building, neural deep learning, Java, C++, Python, LLM quality judgment distillation, customer response modeling, controlled experiment design
Preferred skills
R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, large scale distributed systems (Hadoop, Spark)
Technologies
Java, C++, Python, R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, Hadoop, Spark
Responsibilities
Model customer responses to shopping defects using session data; train compact models against LLM-generated labels within latency/cost budgets; collaborate with ranking teams to integrate quality signals into objectives; design and execute controlled experiments to validate changes.
Seniority
Senior, hands-on IC