Gabriel Moryoussef

As a PhD candidate in Statistics & Data Science at Carnegie Mellon University with a strong mathematical foundation from Oxford University and specialized Machine Learning training from ENS Paris-Saclay (MVA program), I bring a unique combination of theoretical depth and practical expertise to data science projects.

My professional experience includes working as an Applied Scientist at Amazon, where I focused on multi-step reasoning and planning with Large Language Models. This role, combined with my research experience at the European Bioinformatics Institute (EMBL-EBI), has given me extensive hands-on experience with:

Advanced machine learning model development and deployment

Deep learning and neural network architectures

Large-scale data processing and feature engineering

Statistical analysis and modeling

Algorithm optimization and hyperparameter tuning

Biological and medical data analysis

Time series analysis and forecasting

As a Graduate Teaching Assistant at Carnegie Mellon, I've taught courses in Financial Data Science, Natural Language Processing, and Advanced Data Analysis, keeping me at the forefront of current methodologies and best practices.

Programming: Python, MATLAB, Fortran

I specialize in solving complex data challenges across various domains, from financial markets to healthcare analytics. My background in pure mathematics, combined with practical machine learning expertise, allows me to approach problems with both theoretical rigor and practical efficiency.

Available for:

Machine learning model development

Statistical analysis and modeling

Deep learning projects

Data pipeline optimization

Biomedical data analysis

Time series forecasting

Technical consulting

Looking forward to discussing how I can contribute to your data science needs.