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 future closing prices of assets and their peers.• 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 dataset. The model is capable of providing responses in an average of 15 seconds.• Utilized Redis vector database for similarity search, enabling classification of user queries before processing by an LLM with function calling for similar cases.