Quantitative Analyst Intern
• Performed time series analysis to assess market responses to company financial announcements, using K-means clustering to categorize reaction patterns based on key predictive features. • Collaborated with data engineering and research teams to developed scalable ETL pipelines, improving efficiency for time-sensitive financial modeling• Analyzed 1M+ data points using SQL/SparkSQL, applying Z-score standardization, regression, and related statistical analysis for feature engineering, and DBSCAN clustering for anomaly detection and data cleansing• Deployed Fama-MacBeth regression to assess the temporal consistency of features impacting long/short-term returns, utilizing cross-sectional analysis to identify consistently predictive factors and exclude volatile features