saad sadir

Designed and implemented a statistical arbitrage engine using Python and Pandas to identify mean-reverting opportunities across pairs of equities. Built a backtesting framework from scratch to simulate long-short strategies on historical tick-level data, integrating performance metrics like Sharpe ratio, drawdown, and turnover.

Enhanced data quality by cleaning and preprocessing large datasets, improving the reliability and performance of predictive models. Implemented automated workflows using Power Automate to streamline repetitive tasks and improve operational efficiency.

Developed machine learning models to assess credit risk for corporate clients by analyzing historical financial data and identifying key indicators of potential credit defaults. Employed time series analysis techniques to model credit risk trends over time, forecasting future financial instability and improving the accuracy of risk assessments.

Focused on the application of computer science principles to financial markets. Gained expertise in quantitative finance, algorithmic trading, and data analysis relevant to the financial industry.

Completed a rigorous curriculum in advanced mathematics and physics. Developed strong analytical and problem-solving skills, providing a solid foundation for further studies.