Sanjay K.

Quantitative analytics professional with experience running end-to-end projects within the statistical modeling and machine learning domains. Strong technical knowledge of data science methods and related Python libraries. Expert knowledge of financial market applications of quantitative analyses. Possess expertise in the following areas: - Building machine learning models from the ground-up; all types of neural nets - Statistical Analysis of large data sets; Probabilistic modelling of uncertainty - Optimization algorithms for machine and statistical learning, as well as for more traditional models - Financial market and econometric applications; substantial experience evaluating trading strategies in both cash and derivatives markets across all asset classes - Python coding projects, from scratch as well as via the most popular libraries Education: - Undergraduate Mathematics: Stanford University School of Continuing Education - Graduate Mathematics: University of Washington's Applied and Computational Mathematics Masters program - Graduate Statistics: MicroMasters in Statistics and Data Science from MIT/MITx Work Experience: - Director of Research & Portfolio Manager at Gallatin Capital Management - Chief Investment Officer at Youngs Advisory Group - Senior Research Analyst at Harvest Fund Advisors Recent Projects and Research: - LSTM recurrent neural network architecture to model long-term dependencies in equity volatility time series; coded in PyTorch and TensorFlow - Bayesian framework combined with Markov-Chain Monte Carlo modelling to dynamically determine parameters for Risk-Parity asset allocation; coded in PyMC. - Non-smooth optimization problems to determine sparse representations of large and dense data sets; coded in SciPy - Sampling Methods for Stochastic Gradient Descent; applications to Machine Learning