Nicholas Grablevsky

Quantitative Developer @Rockcreek

Washington, DC, US

Engineered comprehensive Python-based portfolio analytics platform featuring automated firm-wide reporting capabilities including risk metrics, performance attribution, CUSUM analysis, exception monitoring, exposure tracking, cluster analysis, and position look-through functionalityDesigned and implemented stress testing frameworks to model portfolio impacts under various market scenarios, including novel stress test for potential AI bubble burst to enable proactive portfolio positioningIntegrated quantitative analytics infrastructure with firm\'s AI system, enabling real-time investment recommendations grounded in rigorous calculations rather than generative outputsDeveloped LLM-powered document processing pipeline with intelligent form extraction capabilities, implementing advanced fuzzy matching algorithms for complex data parsingAuthored research paper analyzing quantum computing investment landscape, introducing novel framework for evaluating opportunities based on research output metricsBuilt web scraping infrastructure to extract and analyze quantum computing publications from arXiv, tracking institutional/corporate research contributions and geographic trends in quantum innovation

Rensselaer Polytechnic Institute

Bachelor's degree