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I’m Oscar

Data scientist with 14 years in analytics and data systems. I build reproducible, explainable models and cloud pipelines using Python, SQL, BigQuery, and GCP (Vertex AI, Docker), partnering with data engineers to translate analytics into dbt models for scalable deployment. Recent work focuses on predictive modeling, causal/experimentation, and executive-ready analytics. Co-author of the eda-toolkit PyPI library. M.S. Applied Data Science (2022).

PRESENTATIONS

• 2025 JupyterCon Co-Presenter
• 2021 Education Data Science Summit Co-Presenter

LANGUAGES

English and Spanish

DATA PROFESSIONAL

Personable, ambitious, and dedicated.

Skillset

• Python • SQL • Machine Learning • Cloud Data (GCP · BigQuery · Vertex AI) • Experimentation (A/B · Causal) • Pipelines (dbt · Docker) • Data Visualization (Looker) • SHAP

 

 

 

Welcome to my data journey!

 

It started in Excel, where mastering formulas like VLOOKUP sparked a lifelong curiosity about how data drives decisions. That curiosity led me to SQL, opening doors to data warehousing, automation, and reporting—early experiences that shaped my analytical foundation.

 

Eager to deepen my expertise, I pursued an M.S. in Applied Data Science (2022), which launched me into predictive modeling, causal analysis, and experiment design.

 

Today, I work in Python, SQL, and BigQuery on GCP (Vertex AI, Docker), collaborating with data engineers to productionize analytics in dbt. My focus is on building reproducible, explainable models and cloud-based workflows that translate data into meaningful insights and measurable outcomes.

Education

Master of Science MS Applied Data Science

University of San Diego

Bachelors Degree Computer Networks

Coleman University

Certifications

CTO Mentor Program Graduate (CCTO)

CITE California IT in Education