Work Experience 🚀
Timeline 📅
Engineer’s Gate
Engineer’s Gate is a New York City-based hedge focused on computer-driven trading in global financial markets.
Quantitative Researcher (Incoming Summer 2025 Intern)
Internship: May 2025 – August 2025
New York, NY
- I will be joining the Alpha Generation Team at Engineer's Gate, a NYC-based hedge fund, this summer to work on Long/Short Equity strategies.
Rostrum Grand Asset Management
B2B investment firm that provides risk-adjusted performance across private and secondary markets investment strategies.
Machine Learning & Data Engineer (Full Time)
Full Time: Jan 2023 – July 2024
Hong Kong City, Hong Kong
- Built OLS-based predictive model with Adjusted R-squared valued >85% using 10+ years of historical and real-time data.
- Accurately forecasted fund performances using analysis of 150+ financial metrics across the portfolio.
- Employed Python scripts with pandas for data cleaning, reducing processing time by 33% and rectifying data quality issues.
- Received the highest performance rating given to top-quartile interns and was offered a full-time role during internship.
WorldQuant BRAIN
BRAIN is a research platform developed by WorldQuant, a global quantitative asset management firm with over $7 billion in assets under management.
Quant Research Consultant (Part-Time)
Part Time: May 2024 – August 2024
Remote
- Conducted quantitative research and backtest trading signals based on momentum, reversal, and volatility to predict global equity performance across various international markets.
- Submitted 50 trading alphas, with 41 used in production, achieving Sharpe > 2, turnover > 25%, and correlation < 60%.
- Hired after Gold Level in WorldQuant Challenge & qualifying for Stage 2 (Top 5%) International Quant Championship, 2024.
Invygo
Invygo is UAE's first & largest car subscription service through an app and web-based experience.
Machine Learning & Data Engineering Intern
Internship: June 2021 - August 2021 (3 months)
Dubai, U.A.E
- Developed a predictive analytics system using ML to forecast inventory demand, achieving a 20% reduction in overstock and a 10% reduction in stockouts.
- Utilized machine learning algorithms to analyze user behaviour patterns, enabling the prediction of churn rates with 90% accuracy and informing targeted retention strategies.
- Worked with product manager to re-architect the software framework to optimize user data funnels and inventory management towards car dealers, boosting revenue by 6% after implementation.