Summary
Amila Mujkic is a data-driven senior analyst in New York with eight years of experience applying Bayesian thinking to investment analytics and trading across securitized products and consumer credit. Trained at Wharton (Quantitative Finance & Statistics) and Penn Engineering (MS Data Science), she blends quantitative finance, probabilistic modeling, and advanced computing to turn noisy data into actionable probabilities. Her background spans trading ABS loans at Barclays, research and teaching roles in probability and deep learning at Penn and Wharton, and hands-on data science work that informs investment decisions at Rialto Capital. Bias-aware and always updating, she’s comfortable translating complex models for practitioners and has a track record of pushing academic methods into real-world trading and analytics workflows. An early competitive programmer and former game developer, she brings both rigorous mathematical instincts and product-minded software skills to solve messy financial problems.
8 years of coding experience
3 years of employment as a software developer
Master of Science - MS Data Science, Master of Science - MS Data Science at University of Pennsylvania
Competitive programming and competitive mathematics, Competitive programming and competitive mathematics at UMKS School for Gifted Students
Bachelor's degree Quantitative Finance and Statistics, Bachelor's degree Quantitative Finance and Statistics at The Wharton School
English, Bosnian, Croatian, Serbian, German, Turkish, Spanish, Albanian