MSc Data Science graduate turning complex data into clear business decisions. I build predictive models and dashboards that help teams act faster and smarter.
Automated extraction of ESG disclosures from Apple, Alphabet, and Tesla 10-K filings. Built NLP pipeline to identify Environmental, Social, and Governance content from SEC EDGAR.
Built a gradient boosting model to estimate claim risk for auto policies. Engineered features from policyholder data that improved premium pricing accuracy and reduced loss ratios.
Developed classification and clustering models to identify false alarms in emergency calls. Delivered actionable insights for deployment planning and resource optimization.
Benchmarked KNN, Random Forest, Logistic Regression, and SVM models. Identified BMD and Age as top predictive factors using feature importance analysis.
Trained sequence-to-sequence model on 45K LRS2 sentences using TPU. Implemented phoneme-viseme features achieving ~3% WER, outperforming baseline by 15+ percentage points.
Evaluated 5 open-source ESG extraction tools against 62 real 10-K filings using a 55-keyword dictionary. Scored on setup, code quality, ESG signal, and SEC compliance.
Deep source analysis of edgartools v5.17.1: 332 files, 657 classes, 21 dependencies mapped. Evaluated XBRL parsing, HTTP layer, AI integration, and ESG extraction capabilities.