Daniel Querales

Data Scientist & MLOps Engineer

Specializing in building end-to-end machine learning pipelines, robust data infrastructure, and production-grade predictive systems. Focus areas include containerized deployments, rigorous model constraints, and automated workflow orchestration.

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Featured Production Work

Bitcoin Time Series Prediction

Built automated frameworks for processing and forecasting volatile digital asset trends, ensuring high performance under strict model execution timelines.

Time Series Python ML Pipelines

Customer Churn Prediction Engine

Developed an end-to-end classification ecosystem utilizing monotonic constraints and feature engineering pipelines to track user retention risks.

XGBoost Feature Engineering SQL

Technical Competence

MLOps & Orchestration

Airflow DAGs Docker GitHub Actions

Modeling & Engineering

XGBoost Python & SQL Databricks