Argentina
Eyisto José Aguilar Trejo
Data Scientist · PhD in Applied Sciences
Data Scientist with a PhD in Applied Sciences and expertise in statistical modeling, quantitative analysis, and complex data systems. Experienced in developing computational models, performing mid-scale data analysis, and extracting actionable insights from high-dimensional datasets with knowledge in Python, SQL, Power BI, and Excel, with hands-on experience in time-series analysis, stochastic modeling, feature engineering, and data visualization. Passionate about leveraging data-driven methodologies to support business decision-making and predictive analytics.
Portfolio
Projects with measured results. Click any of them for the details: methodology, charts, results and demos.
ROC-AUC on test
Axis starts at 0.5 (chance)
Conversion propensity model
Predicts which web sessions end in a purchase (~393k Google Analytics sessions). The first version scored an AUC of 0.988 using the session's own totals; rebuilt with only pre-decision information: AUC 0.878 and ×7.8 lift.
Collectible plant price prediction
A model that suggests price bands for collectible plants from listings across several marketplaces. R² 0.23 on test with the root cause identified, plus an LLM (Gemini) extraction pipeline.
SKU-level sales forecasting
7-day forecast of daily sales for 20 products. A genuinely separate test set with WAPE 0.730 and a ~26% improvement over Seasonal Naive in the 28-day evaluation.
Competition · KaggleAlixPartners Data Challenge: box catalog
Redesign of a fictional company's box catalog (427 products, 5 plants): from 204 to 59 box types and a validated saving of $20.9M per year (9.96% lower cost).
Published productCapital Party
An events guide for salsa, bachata and nightclubs. A React Native app on the App Store and Google Play, fed by a pipeline that extracts events from flyers with Gemini.
Own project · Full-stackInvierteEdu: interactive financial education
An app built with React, Recharts and FastAPI: 12 modules on RSI, MACD, moving averages, P/E, EPS, ROE and dividends, with a live price chart for any stock. The app's interface is in Spanish.
Professional experience
Data Scientist – Quantitative Modeling & Analytics (Consultant)
Oct 2024 – PresentOwn project / Self-Startup · Remote
- Developed statistical and computational models in Python to analyze large-scale complex datasets (~10,000+ nodes).
- Performed exploratory data analysis (EDA) and time-series analysis to detect patterns, correlations, and emergent behaviors.
- Designed quantitative metrics to monitor system states and validate model performance.
- Applied stochastic modeling and simulation techniques to evaluate system dynamics under different scenarios.
- Communicated findings through data visualizations and structured analytical reports.
Tech stack: Python (Pandas, NumPy, SciPy), Statsmodels, SQL, Power BI, Excel, Google Cloud / AWS
Jr. Data Scientist
Aug 2024 – Jan 2026Plants Without Borders · Remote
- Automated the extraction of product sheets (name, price, pot size) with the Gemini API over parsed HTML, independent of each site's structure, with JSON output into SQL.
- Performed exploratory data analysis (EDA) to identify trends, anomalies, and data quality issues.
- Built a price prediction model: baseline (DummyRegressor), linear regression, then Random Forest and gradient boosting, with a log target and a 70/15/15 split evaluated with RMSE.
- Cleaned, transformed, and prepared datasets for analytical modeling.
Tech stack: Python (Pandas, NumPy, scikit-learn), Gemini API, Beautiful Soup, Selenium, SQL (PostgreSQL), Excel, Power BI
Research Assistant | PhD Researcher
Jun 2020 – Dec 2023National University of San Martín · Argentina
- Conducted numerical simulations of neural network models using Python.
- Analyzed avalanche size distributions under varying parameter configurations to identify emergent patterns.
- Performed statistical analysis of simulation outputs to evaluate system behavior.
- Author of 4 articles in Physical Review E (one as first author) and an arXiv preprint.
Tech stack: Python, numerical simulation, statistical analysis
Professor
Mar 2018 – Feb 2020University of the Andes · Venezuela
- Taught undergraduate physics and quantitative methods courses.
- Developed analytical problem-solving frameworks for technical subjects.
Technical skills
Programming & data analysis
- Python (Pandas, NumPy, SciPy)
- SQL
- FastAPI
- Jupyter Notebook
- Git
Statistics & machine learning
- Scikit-learn
- LightGBM
- SHAP
- Statsmodels
- Statistical inference
- Hypothesis testing
- Time-series analysis (ARIMA, autocorrelation)
- Backtesting and temporal validation
- Regression and classification models
Modeling & simulation
- Stochastic modeling
- Monte Carlo simulation
- Numerical methods
- Dynamical systems
Data processing
- Data cleaning and transformation
- Feature engineering
- High-dimensional data
- ETL
- LLM-based data extraction (Gemini API)
Visualization & BI
- Power BI (dashboards and KPIs)
- Advanced Excel
- Matplotlib
- Seaborn
Cloud & tools
- Google Cloud Platform (Cloud Run)
- Docker
- Firebase
- AWS (basic)
Education
PhD in Applied Sciences and Engineering
National University of San Martín
2021 – 2023
Master's in Fundamental Physics
University of the Andes
2017 – 2018
Bachelor's Degree in Physics
University of the Andes
2009 – 2017
Doctoral Research Fellowship – CONICET
Argentina
Certifications and publications
- Finite-size correlation behavior near a critical point: A simple metric for monitoring the state of a neural network — Physical Review E 106, 2022 (first author)
- Similar local neuronal dynamics may lead to different collective behavior — Physical Review E 104, 2021
- Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations — Physical Review E 107, 2023
- Scale-free correlations in the dynamics of a small (N ≈ 10 000) cortical network — Physical Review E 108, 2023
- School on Applications of Nonlinear Systems to Socio-economic Complexity — ICTP South American Institute for Fundamental Research, 2022
- Courses on model-based neural systems & data-driven modeling — XIX Regional Congress on Statistical Physics and Applications to Condensed Matter
Languages
Let's talk about your next data project
Available for data analysis, statistical modeling and machine learning projects.