On the Core Search Algo research team, specifically focused on eCommerce product search ranking R&D. Primary responsibilities:- Ranking model development. Tree-based models, neural models, learning-to-rank frameworks, etc.- Ranking signal/feature development. Engagement features, content features, etc.- Cutting-edge algorithm innovations. LLMs.Main outcomes:- Launched multiple new models and features.- Major GMV and conversion lifts.- Major NDCG gains.- Publications.- Awards.

Search Ranking Algorithms

Machine Learning @ FICC trading• Built end-to-end machine learning pipelines (data – model – inference – application) for classification and regression; use cases include bond markdown prediction, asset pricing, and collateral valuation.• Developed in-house NLP packages with deep learning frameworks for text embedding and classification.• Performed daily model tuning with revolving data; improved various deployed models, e.g., Logistic Regression, SVM, Random Forest, Gradient Boost, and XGBoost, etc.

AI CoE - NLP• Delivered multiple machine / deep learning developments on firmwide unstructured and structured data of large scales to increase the ROI for various lines of businesses, e.g., Risk, Investment Management, etc.• Developed multiple modules and tools for internal capabilities, e.g., topic modeling, auto hyperparameter tuning, visualizable labeling and annotation, etc.; contributed all to the firmwide reusable codebase.• Performed various, constant ad-hoc data analyses and communicated the results to the business partners and the leadership with positive feedback.