Summary
Andrew Acosta is an experienced data scientist and quantitative analyst with 11+ years of applied expertise in capital markets, energy markets, and enterprise risk modeling, currently mentoring and authoring while consulting on CCAR/DFAST compliance and OTC derivatives pricing. He blends deep statistical and econometric skills with hands-on coding in Python, R, C++, MATLAB and cloud tools (AWS, Hadoop) to build predictive models, Monte Carlo simulations, and yield-curve/option-pricing systems for banks, trading firms, and energy ISOs. Notable strengths include power market LMP modeling for PJM, practical implementation of regulatory stress tests, and translating complex cashflow valuations into auditable code and documentation. A seasoned mentor and former adjunct instructor, he pairs academic rigor (MS Financial Engineering, PhD work in Knowledge Management, coursework at IIT/MIT/UChicago/Princeton) with pragmatic delivery that has saved clients millions in infrastructure and regulatory costs. He is also an active practitioner-author contributing to data science literature and mentoring the next generation of analysts.
11 years of coding experience
17 years of employment as a software developer
M.S., Financial Engineering, M.S., Financial Engineering at Illinois Institute of Technology
Doctor of Philosophy (Ph.D.), Knowledge Management, Doctor of Philosophy (Ph.D.), Knowledge Management at Walden University
Econometric Analysis, Econometric Analysis at University of Chicago
Analysis of Algorithms, Analysis of Algorithms at Princeton University
Real Analysis, Real Analysis at Massachusetts Institute of Technology
MBA, Finance, MBA, Finance at Roosevelt University
Spanish, English