Data Scientist
Current- Developed a multi-asset price volatility regression model using gradient boosting, achieving a Mean Absolute Error (MAE) of under 0.5% in volatility prediction. Additionally, leveraged a decoder-only model to forecast.
- Created an LLM agent to generate equity research reports by simulating analyst research process. This involved integrating anomaly detection results as part of the feature engineering within the company’s fundamental.
- Utilized Redis vector database for similarity search, enabling classification of user queries before processing by an LLM with function calling for similar cases.