Mayank Mahajan

• Conducted research on systematic, automated trading signals using statistical analyses involving large amounts of structured and unstructured data from a myriad of sources and vendors. Formulated new signal implementation details and implemented them in algorithms.
• Quantitatively determined the effects of model and signal improvements using backtests and historical returns to predict future performance and presented results and final investment proposals to executives across the firm.
• Performed deeper retrospective analyses, extracting key insights for managers and partners to present to clients.

• Developed proprietary investment strategy (data processing, signal generation, and model creation) from scratch using Python and Excel resulting in 80-100% boost in Sharpe Ratio. Quantitatively evaluated performance and risks of the improved model through multivariate linear regressions and significance tests.
• Synthesized technical results and presented strategic recommendations to senior partners and managers of the firm.

• " Optimized single and connection model GraphQL server requests via a proxy batch service in Objective-C++ and significantly decreased the startup time of the iOS application. Reduced time to interact (TTI) by 2%, and TTI outliers by 8%.

• " Created and released a transition matrix function (TMX) on the Bloomberg Terminal using JavaScript to model delinquencies in a mortgage-backed security deal. Presented TMX to sales, and it had hundreds of users after launch.
• " Diagnosed and fixed the improper parsing of a $130 million agency deal within the Python data service of the system so it could be issued on the Bloomberg Terminal, thereby preventing the deal from going to a competitor.

Concentration in Statistics and Machine Learning

Attended on full scholarship.

Academics: GRE - 169Q, 161V, 5.0W

Major GPA: 3.8/4.0, Cum. GPA: 3.7/4.0

Relevant Coursework: Statistical Methods in Finance, Linear Regression Models, Advanced Data Analysis, Big Data Analytics, Machine Learning, Natural Language Processing

GPA: 3.95/4.00, SAT: 2400/2400