Nathan Aguirre

Built recommender systems, scoring algorithms, and unsupervised clustering methods. Models used include Factorization Machines, GBTs, Random Forest, HDBSCAN, SVCs, etc. Additional focus on text analysis and image similarity using deep learning methods. Built internal products using RNNs, LSTMS, Attention networks, CNNs, etc.

Data scientist with a focus on financial and alternate data for trading analysis/backtesting and time-series forecasting. Other topics include customer loyalty modeling, telematics-based geospatial analyses, etc.

Research and coursework in machine learning, data science, and network analysis. Analyzing full-texts of scientific publications to determine relationships between primary papers and related references. Analyzed distributions of in-text citations using multiple techniques including time-series analytics, bootstrapping, NLP, and hypothesis testing methods to determine multi-regime citation profile.