Experience
Machine Learning Scientist — Morgan Stanley
Feb 2022 – Present · New York, NY
- Deep learning models for structured financial data
- tabular data prediction with representation learning
- contextualized factor analysis and time series forecasting
- nowcasting on mixed-frequency and/or irregularly-sampled data
- Efficient Monte Carlo simulation using ML-assisted control variates - variance reduction, faster runtime
- Small-scale recommender systems for fixed-income asset sales
Quant Modeler / Strategist — Goldman Sachs
May 2018 – Feb 2022 · New York, NY
- Scenario design and generation for macroeconomic variables & asset price/spread indices
- econometrics models / time series models
- joint modeling of a dynamical system
- PnL attribution using factor models and cross-sectional regressions
- Portfolio liquidation in relation to credit spread via survival models
- Revenue forecasting (time series) and corporate loan pricing (DCF + options)
Miscellaneous
Reviewer Service
I served as a referee for the following journals over the years:
- Journal of the American Statistical Association (JASA)
- Biometrika
- Econometrics and Statistics (ECOSTA)
- International Statistical Review (ISR)
- Journal of Computational and Graphical Statistics (JCGS)
- Transactions on Machine Learning Research (TMLR)
Quant Strategist Summer Intern - Goldman Sachs
Summer 2016; Summer 2017
- Investment Banking Division (June - August 2017)
- Model Risk Management (June - August 2016)
Graduate Student Instructor / Research Assistant
Sept 2013 - April 2015; May 2015 - April 2018
- Lab instructor for Intro to Statistics and Data Analysis (STAT250); Statistical Computing (STAT406); Data Mining (STAT415)
- Research Assistant for Dept. of Statistics; Dept. of Computational Medicine and Bioinformatics; UM Health System, Brehm Center for Diabetes Research
- Interdisciplinary papers produced: Sas et al. 2018, Afshinnia et al. 2019, Sas et al. 2021