Rushi Glasswala

AI Researcher

Surat, Gujarat, India0 mo experience

Key Highlights

Built multi-strategy trading systems.
Developed AI-powered trading tools.
Strong foundation in quantitative research.

Stackforce AI infers this person is a Fintech professional with expertise in quantitative research and machine learning.

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Skills

Core Skills

Financial ModelingMachine LearningNatural Language Processing

Other Skills

Algorithmic TradingAnomaly DetectionBlockchainComputational FinanceCorrelation AnalysisDeep LearningFinancial MarketsFine TuningForecastingGraph Traversal TechniquesLarge Language Models (LLM)NER Model DevelopmentNatural Language Processing (NLP)Predictive AnalysisRisk Analysis

About

I'm Rushi Glasswala, a recent IIT Gandhinagar graduate with a strong foundation in quantitative research, machine learning, and financial modeling. I’ve built multi-strategy trading systems, fine-tuned language models for financial data, and developed tools for market forecasting and risk analysis. My work bridges data, code, and financial logic — whether it's analyzing time series, extracting insights from economic texts, or deploying AI-powered trading tools. I’m driven by clarity, curiosity, and building things that actually work in the real world.

Experience

Worldquant

Quantitative Research Intern

Sep 2025 – Present · 6 mos · Mumbai, Maharashtra, India · On-site

Universidade federal de goiás

Data Science Intern

Jun 2024 – Jan 2025 · 7 mos · Goiânia, Goiás, Brazil · Remote

I worked on financial time series for NIFTY50 and IBOVESPA — performing decomposition, stationarity testing, and predictive analysis using Cointegration and Granger causality. I built anomaly detection models, developed a multivariate VAR forecasting framework, and designed an optimized trading strategy evaluated with key risk metrics.

Time Series AnalysisPredictive AnalysisAnomaly DetectionVAR ForecastingTrading Strategy DevelopmentFinancial Modeling+1

Georgia tech financial services innovation lab

Research Assistant Intern

May 2024 – Dec 2024 · 7 mos · Atlanta, Georgia, United States · Remote

I contributed to the KG-QAGen benchmark (arXiv publication) for evaluating LLMs on financial documents. I annotated complex credit agreements, developed a Transformer-based NER model improved via RAG and vector search, and applied graph traversal techniques for multi-hop QA and knowledge extraction.

Natural Language ProcessingMachine LearningGraph Traversal TechniquesNER Model Development