Kshitiz Garg

Operations Associate

New Delhi, Delhi, India3 yrs 4 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Strong foundation in quantitative finance and machine learning.
Hands-on experience in predictive modeling and algorithm development.
Passionate about financial analytics and trading systems.

Stackforce AI infers this person is a Fintech professional with a focus on quantitative research and machine learning applications.

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Skills

Core Skills

Quantitative ResearchMachine LearningData Analysis

Other Skills

Algorithm DevelopmentC++Cascading Style Sheets (CSS)CommunicationComputer ScienceData ScienceDeep LearningEntrepreneurshipEquity TradingExploratory Data AnalysisFeature EngineeringFinancial Data AnalysisFront-End DevelopmentGARCHHTML

About

I'm a passionate and self-driven undergraduate at Delhi Technological University (DTU) with a strong interest in Quantitative Finance, High-Frequency Trading (HFT), and Machine Learning. My academic background is complemented by hands-on experience in building predictive models, researching volatility patterns, and developing low-latency data pipelines. As a Quantitative Research Intern at Share India Securities Ltd., I worked on real-time financial data analysis and algorithmic strategy development, gaining practical exposure to market microstructure and signal generation. I'm skilled in Python, Pandas, NumPy, XGBoost, LightGBM, and Streamlit, and I enjoy building efficient tools that bring insights from complex datasets. Currently, I’m exploring opportunities in quant research, trading systems, and ML-driven financial analytics, where I can combine my technical strengths with my passion for markets. Let’s connect — I’m always open to collaboration and conversations around finance, AI, and trading.

Experience

Share india securities ltd

Quant Research intern

Jun 2025 – Jul 2025 · 1 mo · On-site

Designed and implemented predictive models for implied volatility (IV), spot prices, ROCE, and realized volatility (RV) using advanced machine learning algorithms including XGBoost, LSTM, LightGBM, and GARCH variants.
Worked extensively on high-frequency NIFTY market data, applying time-series forecasting techniques to extract and analyze volatility patterns.
Reproduced and implemented cutting-edge quant research papers focused on volatility modeling and high-frequency trading strategies.
Conducted extensive feature engineering using realized variance, autocorrelation, and momentum-based indicators; integrated into predictive pipelines for backtesting and evaluation.
Collaborated with senior quants and traders to align model outcomes with real-world trading signals and execution frameworks.

Predictive ModelingAlgorithm DevelopmentFinancial Data AnalysisMachine LearningTime-Series ForecastingFeature Engineering+1