Summary
Rasool Tahmasbi is a PhD-trained data scientist and Member of Technical Staff in San Francisco with two decades of experience building ML, AI, and statistical systems for complex real-world problems across domains from genomics and gold-mining telemetry to sales, marketing, and finance. He designs hybrid, production-ready pipelines that combine fine-tuned LLMs, transformers, deterministic pattern engines, graph/causal models and vector databases to deliver scalable, cost-efficient semantic classification, root-cause analysis, and automated remediation at extreme scale. At Palo Alto Networks he led causality-driven RCA and large-scale case-deflection platforms that delivered measurable savings and moved research toward publication; more recently he’s architected agentic, retrieval-grounded LLM systems for financial portfolio automation and enterprise data governance. Comfortable spanning research and product delivery, he mixes rigorous statistical foundations (PhD in Statistics) with hands-on systems work—building Linux-based appliances, MLOps stacks, and multi-agent orchestration for real user workflows. A less obvious strength is his knack for combining causal graphs with LLMs to make model outputs explainable and action-oriented, enabling automated policy creation and remediation rather than just predictions.
10 years of coding experience
9 years of employment as a software developer
Amirkabir University of Technology
Postdoctoral Statistical genetics/AI, Postdoctoral Statistical genetics/AI at University of Colorado Boulder
English