Data Science meets
Full-Stack Execution.
Diego Villagran Salazar — Data Scientist & Full-Stack Developer.
I build machine learning systems, analytics products, and scalable web apps that create measurable business impact.
Featured Projects
Selected systems // 2024—2025
SYS_000TimeUp // Time Tracking SaaS
Delivered a stable, production-adopted system that reduces payroll administrative work by up to 80%.
SYS_001COVID-19 Risk Profiles (Mexico)
9 interpretable profiles (K-Means) and 2 risk groups (FCM); documented limitations and reproducible notebooks for Advanced Data Analytics, ESCOM-IPN.
SYS_002NYC Ride-Hailing Analytics Dashboard
Delivered fare prediction with R² > 0.85 and airport classification with 92% accuracy for practical decision support.
SYS_003India Air Quality ETL Intelligence System
Processed 2M+ daily records from 500+ sensors and transformed noisy streams into consistent, actionable health indicators.
Impact & Architecture
Built a two-way architecture (Business Portal & Employee Landing) with dashboards, automated calculations, and cloud infrastructure.
Businesses and employees needed an agile, frictionless system to manage time logs and payments, replacing paper completely.
Impact & Architecture
K-Means and Fuzzy C-Means on stratified samples from 30M+ records; reproducible pipeline from raw data to LaTeX report and Next.js portfolio.
Public health and triage needed multivariate risk profiles from open SSA data—not just isolated factors—to prioritize ICU and resources.
Impact & Architecture
Built an interactive Streamlit analytics platform with predictive ML models, geospatial maps, and multi-tab operational dashboards.
Transportation stakeholders needed reliable insights across Uber and Lyft trip patterns, pricing, and airport operations in New York City.
Impact & Architecture
Designed a cloud ETL architecture using Azure Databricks, PySpark, PostgreSQL, and BI reporting for continuous analytics.
Raw environmental data from hundreds of IoT sensors was fragmented and difficult to convert into policy-ready insights.
Architecture & Scale
Machine Learning Pipelines
From preprocessing and feature engineering to training, evaluation, and deployment of predictive models.
Data Engineering
ETL orchestration with Python, PySpark, SQL, and cloud platforms for reliable high-volume analytics workflows.
Analytics Products
Interactive dashboards and decision systems with Streamlit and Power BI focused on real-world business metrics.
Web Platform Development
Scalable full-stack applications with Next.js, React, TypeScript, and cloud-ready deployment practices.
Working Principles
"Turn complex data into clear decisions and scalable products.
Impact over output
I prioritize measurable outcomes: model accuracy, decision quality, processing speed, and business value.
End-to-end ownership
I build complete systems, from data collection and cleaning to production deployment and monitoring.
Clarity at scale
Good architecture keeps complexity contained so teams can iterate quickly without breaking reliability.