2023 - 2025: Master of Science in Computer Engineering, New York University Courses: Probability and Statistics, Stochastic calculus, Algorithmic Portfolio management, Algorithmic Trading and Quantitative strategies, Machine learning, Deep learning
2019 - 2023: Bachelor in Computer Science, Bharati Vidyapeeth University
Skills & Interests
Skills: C++, Python, SQL, Machine Learning, Deep Learning, Statistical testing, Financial Modeling, Time series analysis, Probability, Risk modeling, MS Excel
Teaching Assistant, R in Finance (FRE-GY 6871), NYU Tandon School of Engineering (Mar 2024 - May 2024)
Assisted the instructor in teaching a graduate-level course on applying R programming and machine learning techniques and guided students on coding exercises, data analysis, and financial modeling.
Reviewed and provided feedback on student assignments involving Monte Carlo simulations, Value at risk, risk modeling, principal component analysis, and regression analysis on financial products such as CDOs and Treasury bonds.
Data Science Intern, United Nations (UNDP) (Jul 2022 - Jan 2023)
Conducted statistical analysis using STL (Linear regression) on large-scale geospatial data to evaluate the impact of climatic factors on agricultural yields enabling access to crucial climate information for ~33,000 farmers saving $4 million.
Designed web scraping scripts and did a trend analysis on topics of interest of posts from the UN's top social media channels using Natural language processing, enabling data-driven decision-making and contributing to a 40% increase in reach and engagement on global digital platforms.
Developed a fundamental factor model incorporating Profitability, Dividend Yield, Value, Market Sensitivity, Medium-Term Momentum, Short-Term Momentum, Volatility, Volume and Liquidity.
Improved portfolio allocation through orthogonalization of high turnover factors, reducing risk from correlated assets and also increasing explainability of each factor's return.
Conducted cross-sectional regression to estimate factor returns, achieving an impressive annual return of 21% and a Sharpe ratio of 1.109, with a significant R-squared metric of 0.11.
Evaluated model performance using statistical tests like QLIKE random portfolio tests, minimum variance portfolio tests on complete data, and clusters of high and low turnover factors.
Developed a market impact model based on Direct Estimation of Equity Market Impact (Almgren et. al) by analyzing trades and quotes data from the top 500 most liquid stocks in NYSE, utilizing daily imbalance as a proxy for trades, and separating temporary and permanent impact components.
Performed non-linear regression to estimate model parameters to calculate temporary impact using Daily value traded, order flow imbalance, volatility, and volume-weighted terminal and starting prices, resulting in improved execution strategies and effective cost estimation.
Natural Gas & Default Prediction, Quantitative Research
Engineered a pricing model for natural gas contracts using volatility forecast using GARCH, storage costs, temperature, and transaction costs.
Created a gradient-boosting model to estimate a borrower's default probability and manage credit risk by predicting estimated losses on default with an accuracy score of 99.56%.
Enhanced default risk modeling by strategically bucketing FICO scores using dynamic programming resulting in more effective risk assessments for loan customers.
Certifications
Financial Engineering and Risk Management (Columbia), Financial Markets (Yale), Bloomberg Capital Markets, Quantitative Modeling and Finance for Analysts (Wharton)