Open to opportunities

Daniel Colín García

@danielcolingarcia

Data Analyst & ML Engineer building statistical models, automated data pipelines, and BI dashboards for decision-ready insights.

Mexico

Message

What I'm looking for

I’m looking to build end-to-end analytics and decision support—data pipelines, statistical/ML modeling, and dashboards—while collaborating with cross-functional teams. I want work that values reproducibility, clear storytelling, and real stakeholder impact.

I’m a Data Analyst & ML Engineer with a background in Mechatronics Engineering and a PhD in Engineering Sciences focused on autonomous systems, sensor fusion, and data-driven decision-making. I build statistical and forecasting models—regression, time series, and classification—then translate results into actionable insights.

At Tecnológico de Monterrey, I develop Python/SQL data pipelines, automate data validation and reporting workflows, and deliver decision support through 10+ dashboards and analytical reports using Power BI and Looker Studio. I also support stakeholders with structured visual narratives and rigorous statistical reasoning, from trend and anomaly detection to hypothesis testing for evidence-based program decisions.

Experience

Work history, roles, and key accomplishments

Current

Data Analytics & ML Engineer

Current

Tecnológico de Monterrey

Aug 2023 - Present (2 years 10 months)

Developed Python/SQL data pipelines for 5+ institutional datasets (thousands of monthly records) and built regression, time-series, and classification models to evaluate system performance trends. Automated data validation and reporting workflows, created 10+ Power BI/Looker Studio dashboards, and communicated results to stakeholders across 4 departments.

Python Pandas NumPy SQL Machine Learning Time Series Models regression Power BI Looker Studio Data Pipelines

Monitoring & Analytics Analyst

Tecnológico de Monterrey

Jan 2023 - Jul 2023 (6 months)

Analyzed institutional datasets to identify trends, anomalies, and performance drivers using SQL and Python, supporting evidence-based academic program decisions. Improved reporting infrastructure and documentation standards to accelerate iteration across analytical teams.

SQL Python Data Analysis Statistical Modeling Hypothesis Testing Time Series Analysis Documentation Standards