Programming Experience in Quantitative Problems

PhD Research at the University of California, Riverside (Jul ‘21 - Ongoing)

• Brief Description
  • Developed new methods of forecasting using cutting-edge mathematical tools such as Reproducing Kernel Hilbert Space.
  • Designed multi-layered tractable deep learning framework for causal inferece problems.
• Skills Used
• R Programming: Computationally efficient function writing, high-speed parallel computing, optimization, np (non-parametric) package, out of sample model evaluations.
• Python: PyTorch, autoencoders, hyperparameter tuning, Scikit-learn, GPU-based computations.

Quant Consultant, Research Triangle Institute (RTI) International (Jul ‘21 - Jun ‘22)

• Brief Description
  • Developed statistical models for future cash flow streams to assist in a $10 million investment decision problem.
  • Designed a missing value treatment methodology to determine the universe of the customer base.
• Skills Used
  • R Programming: Coding, Clean Documentation, Debugging.
  • Statistics, Mathematical Modeling, Problem Solving, Data Analysis, Quantitative Research.

Quant Consultant, Asian Infrastructure Investment Bank (Jul ‘19 - Jun ‘21)

• Brief Description
  • Developed mathematical models to guide statistical pursuits of optimal solutions to investment problems.
  • Provided statistical insights on fixed income and portfolio optimization problems in city planning projects.
  • Developed a statistical framework to select the top 50 cities for potential industrial hubs in Bangladesh.
• Skills Used
  • R Programming: tidyverse, ggplot2, Base R, dplyr, e1071, randomForest.
  • Optimization, Portfolio Management, Quantitative Analytics.
  • Fixed Income Strategies, Regression Analysis, Analytical Skills.

Quant Research Intern, KPMG India (Jan ‘20 - Aug ‘20)

• Brief Description
  • Solved an expected revenue estimation problem using a constrained optimization framework in Python.
  • My method improved R-squared by 15% relative to an alternative model featuring neural networks.
• Skills Used
  • Machine Learning, Constrained Optimization, Predictive Modeling, Resampling Methods, Sensitivity Analysis.
  • Python Programming: scikit-learn, NumPy, Pandas, Matplotlib.

C++ Software Engineer, HCL Technologies, India (Oct ‘16 - Jul ‘17)

• Brief Description
  • Applied object-oriented programming concepts in C++ and Java to solve business problems.
  • Learned good coding practices, including writing clean, well-documented code for reproducibility.
• Skills Used
  • C++, Java, Object-Oriented Programming, Iterations, Conditional Statements.

Formal Programming Courses Taken

• Fundamentals of Object Oriented Programming Using C++: Indian Institute of Technology, Roorkee.
• Data Structures: Indian Institute of Technology, Roorkee.
• Assembly Level Programming and Subroutines: Indian Institute of Technology, Roorkee.
• Statistical Learning with R: Indian Statistical Institute.
• Computational Learning in Python: University of California, Riverside (audit).
• Statistical Computing with R Programming: University of California, Riverside (audit).
• Deep Learning: Coursera.