About Me

I was fifteen when my father, an investor, made me his research partner. We'd spend evenings analyzing balance sheets, debating valuations, and building conviction on positions. That early discipline - turning raw data into investment decisions - is still what drives me today.

I now focus on building systematic, research-driven investment strategies that sit at the intersection of quantitative methods and fundamental intuition. My work spans portfolio construction (Mean-Variance, Black-Litterman, factor models), risk analytics (VaR, drawdown analysis, regime detection), and macro-aware signal design - most recently a multi-factor QQQ strategy that achieved a 1.55 Sharpe on blind out-of-sample data, earning a Quanta Ventures Fellowship selection (Top 5%).

I'm looking to bring this toolkit to an asset manager, bank investment team, or wealth management strategy desk - anywhere that values decision-ready portfolio research, disciplined risk frameworks, and someone who can translate complex quantitative analysis into clear investment narratives.

When I'm not building models, you'll find me at PingPod with a ping pong paddle, watching Max Verstappen in F1, or deep in a strategy game.

  • Portfolio & Risk
    Portfolio Construction, Mean-Variance Optimization, Black-Litterman, Factor Models (Fama-French), Risk Attribution, VaR/CVaR, RAROC, Stress Testing, Scenario Analysis, Regime Detection
  • Quantitative Methods
    Statistical Modelling, Time Series Econometrics (ARIMA, VAR, GARCH), Optimization, Machine Learning (XGBoost, Random Forest, SVR, Neural Networks), Monte Carlo Simulation
  • Technical
    Python (Scikit-learn, Pandas, NumPy, Matplotlib), R, SQL, KDB+/q, VBA, C++, Apache Spark, MongoDB, Neo4j, Linux
  • Platforms & Visualization
    Bloomberg Terminal, Refinitiv Workspace, Tableau
  • May 2025 - Jan 2026
    Quantitative Data Analyst at Insyst, INC
  • Jun 2024 - May 2025
    Quantitative Risk & Portfolio Analyst at Ascot Group LLC
  • Jun 2023 - Jun 2024
    Quantitative Strategist (Research) at Columbia University
  • Jan 2024 - May 2024
    Investment Banking Intern at Nova Capital
  • Mar 2022 - May 2022
    Business Intelligence Analyst at AMN life Science
  • Jan 2021 - Jun 2021
    Equity Research Analyst at CLSA
  • Dec 2019 - Aug 2020
    Econometrics Research Associate at Meghnad Desai Academy of Economics
  • Sept 2022 - Dec 2023
    MSc in Applied Analytics - Columbia University
  • Aug 2019 - Aug 2020
    PGDM in Econometrics (Quantitative Economics) - Meghnad Desai Academy of Economics
  • Jan 2019
    Chartered Financial Analyst - Passed level 1 (CFA Institute)
  • May 2016 - June 2019
    BSc in Finance - Mumbai University

My Projects

Systematic QQQ Strategy with Macro Regime Awareness

Multi-factor trading strategy achieving 1.55 Sharpe on blind holdout (2022–25) vs QQQ's 0.52. Uses Fed liquidity signals, Parkinson volatility efficiency, inflation veto, and bear trap detection. Quanta Ventures Fellowship Finalist (Top 5%).

Active Portfolio Management

Managed a paper-traded portfolio of 15 stocks, beating the S&P 500 index by 3%. Back tested using advanced models (Index, Mean-Variance, FAMA French, Black Litterman, CAPM, Equity Valuation), algorithmic trading, and machine learning (XGBoost, SVR, Neural Networks)

Credit Risk Modelling

Computed a Credit risk management model using L1, L2 penalized logistic regression with an accuracy of 93.4%. Predicted Loan delinquency paving the way for more informed credit monitoring

Algorithmic Trading using Machine Learning

Engineered a data-driven trading strategy using XGBoost and Random Forest algorithms to analyze 5 feature categories across 100+ stocks, leveraging Python (Pandas, NumPy, Matplotlib) for data processing and visualization.

r/wallstreetbets Sentiment Analysis

Spearheaded an ARIMA-based Time Series Analysis to predict 10 tech stocks. Analyzed r/wallstreetbets sentiments (Affin: 0.34, Jocker: 0.10), noted 437 more puts than calls, hinting bearishness. Found 0.6 similarity between predictions and sentiments.

Real-Time Stock Trading Analysis with Apache Spark

Designed a real-time stock trading application using Python and Apache Spark, and analyzed a multi-million dataset of 6875 stocks, incorporating risk management into trading strategies in under 1.5 minutes.

Certifications

A selection of professional certifications and important credentials.

CFA Level I (Passed)

Chartered Financial Analyst - Passed Level I (Jan 2019)

Akuna Capital University

Options 101

Options Pricing, option greeks, hedging strategies

KDB+/q

KDB+/q Developer Level 1

The world's fastest vector based in-memory database for financial analytics

Contact Me

ij2243@columbia.edu

+1(917)216-7363

Download CV