Heath Thapa is a quantitative architect and AI leader dedicated to transforming complex data into a competitive market advantage. His work spans building Machine Learning, Generative AI systems, factor-based research, portfolio optimization and factor signal extraction using LLMs, with strong focus on translating research into scalable, real-world products. Specialing in the deployment of Agentic AI frameworks and RAG-based LLMs, Heath transforms structured and unstructured financial data into actionable investment signals.
Areas of focus include: • Generative AI & Agentic AI Systems • Large Language Models (LLMs) & RAG Architectures • AI Copilots & Workflow Automation • Machine Learning & Deep Learning • Financial Economics & Quantitative Research • Asset & Wealth Management Analytics • Capital Markets Intelligence • Portfolio Analytics & Forecasting • MLOps, Cloud Engineering & AI Governance • Product Analytics, SaaS & Mobile Intelligence
- Quantitative Methods: Factor Models, Time-Series Analysis, Regression, Decision Trees, Random Forests, Sentiment Analysis, TimeSeries Forecasting, Optimization, Cluster Analysis, Measurement Plan
- Optimization and Automation: Data Pipelines, Machine Learning Automation
- Financial And Customer Analytics: ML Ops, Asset Management, Financial Modeling, Forecasting, Trading Strategies, Media Mix Modeling, Causal Inference
- Data Analysis & Visualization: SQL, Python, R, SAS, Google Analytics, Tableau
- Quantitative Finance (https://github.com/singularity-htmagarh/quant-finance)
- ETFs Portfolio Tracker (https://github.com/singularity-htmagarh/etf-portfolio-tracker)
- Data Science Portfolio (https://github.com/singularity-htmagarh/data-science-portfolio)