Key Responsibilities
• Develop and optimize quantitative models and algorithms for pricing, risk management, and trading strategies.
• Conduct research on time-series analysis, stochastic processes, PDE-constrained optimization, and statistical methods.
• Implement high-performance computational solutions using programming languages such as C++ and MATLAB.
• Apply machine learning and data mining techniques to extract insights from large datasets.
• Collaborate with other researchers and stakeholders to present findings and recommend data-driven strategies.
• Enhance existing models through variance reduction techniques, parallel computing, and algorithmic improvements.
• Stay updated with academic and industry advancements in quantitative finance and integrate relevant innovations into practice.
Qualifications
• Ph.D. in Computational and Applied Mathematics, Mathematics, Financial Engineering, or a related field.
• 5+ years of experience in quantitative research within the financial sector.
• Strong proficiency in C++, MATLAB, and experience with parallel computing (e.g., MPI).
• Solid understanding of stochastic calculus, numerical methods, optimization, and Bayesian inference.
• Experience with Monte Carlo methods, Kalman filtering, PCA, and other statistical techniques.
• Demonstrated ability to develop and implement efficient algorithms for large-scale problems.
